## Qualifications
* Strong medical knowledge across various specialties
* Excellent communication and attention to detail
* Ability to work independently in a remote setting
* Technical aptitude for AI tools and data annotation
## Benefits
* Flexible remote work environment
* Competitive compensation
* Opportunity to contribute to innovative healthcare technology
* Collaborative and supportive team culture
## Responsibilities
* Collaborate with AI development teams to refine medical algorithms and ensure accuracy
* Review, validate, and annotate medical data to improve AI performance
* Provide expert insights on medical terminology, procedures, and best practices
* Conduct quality checks and feedback loops to optimize AI outputs
* Stay updated on medical advancements and integrate relevant knowledge into training processes

## Full Description
We are seeking a Remote Medical General Expert - AI Trainer based in Anaheim, California, to enhance AI medical models through expertise and training.

• Collaborate with AI development teams to refine medical algorithms and ensure accuracy.

• Review, validate, and annotate medical data to improve AI performance.

• Provide expert insights on medical terminology, procedures, and best practices.

• Conduct quality checks and feedback loops to optimize AI outputs.

• Stay updated on medical advancements and integrate relevant knowledge into training processes.

Skills Needed

• Strong medical knowledge across various specialties.

• Excellent communication and attention to detail.

• Ability to work independently in a remote setting.

• Technical aptitude for AI tools and data annotation.

Benefits

• Flexible remote work environment.

• Competitive compensation.

• Opportunity to contribute to innovative healthcare technology.

• Collaborative and supportive team culture.



Qualifications
• You have an M.Sc
• focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field
• 3+ years of production experience in ML Infra, DataOps, distributed training
• Expertise in programming languages and tools critical for high-performance computing in Python/C++ and machine learning including Deep Learning frameworks like PyTorch /TensorFlow/JAX
• Ability to understand deep learning algorithms, e.g. in natural language processing, reasoning
• Familiarity with Azure/AWS/GCP cloud products for MLOps and DataOps pipelines
• Proficiency with Kubernetes clusters and distributed compute assets
• Strong communication and teamwork skills
• Readiness to explore and promote cutting edge technologies in ML Infrastructure domain and beyond
• Multi-node and multi-GPU training
• Mathematical Reasoning - discrete math and logic
• Formal Verification - lean
• 9 more items(s)
Benefits
• Demonstrated publications in any of the major conferences
Responsibilities
• You'll work closely with a talented team of AI experts, EBM specialists, formal verification engineers, and software developers to create groundbreaking solutions
• Research new reasoning algorithms and models
• Develop model benchmarking processes and tools
• Build effective and efficient ML data pipelines
• Adjust frameworks and interfaces to accelerate machine learning development
• Develop the infrastructure for data augmentation pipelines and synthetic data generation
• Collaborate with other teams to understand their pain points and priorities to define milestones of the corresponding roadmaps
• Derive practical solutions and integrate them with the results of other teams to provide the best overall resolution
• 5 more items(s)
More job highlights
Job description
Who we are

At Logical Intelligence, we're revolutionizing software development with AI-powered formal verification. We've developed groundbreaking agents that provide mathematical guarantees of code correctness, ensuring that software behaves exactly as intended while proactively identifying bugs and security vulnerabilities. Our novel foundation model enables scalable, precise reasoning for form...ally verifiable code across Rust, Golang, and smart contract VMs. We've won a well-known formal verification benchmark called PutnamBench, which consists of 672 hard math problems from the William Lowell Putnam Exam, the oldest collegiate mathematics competition in North America. Backed by a world-class team - including ICPC champions, a Fields Medalist and an ACM Turing Award winner - we're building the future where all code is provably correct.

About the role

Join our team as an AI Engineer and help us push the boundaries of what's possible in logical reasoning! We're looking for a motivated individual to design and refine the data and ML pipelines for scaled distributed training and validation of ML models. You'll work closely with a talented team of AI experts, EBM specialists, formal verification engineers, and software developers to create groundbreaking solutions.

What you'll do

• Research new reasoning algorithms and models

• Develop model benchmarking processes and tools

• Build effective and efficient ML data pipelines

• Adjust frameworks and interfaces to accelerate machine learning development

• Develop the infrastructure for data augmentation pipelines and synthetic data generation

• Collaborate with other teams to understand their pain points and priorities to define milestones of the corresponding roadmaps

• Derive practical solutions and integrate them with the results of other teams to provide the best overall resolution

Qualifications

• You have an M.Sc. focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field

• 3+ years of production experience in ML Infra, DataOps, distributed training

• Expertise in programming languages and tools critical for high-performance computing in Python/C++ and machine learning including Deep Learning frameworks like PyTorch /TensorFlow/JAX

• Ability to understand deep learning algorithms, e.g. in natural language processing, reasoning

• Familiarity with Azure/AWS/GCP cloud products for MLOps and DataOps pipelines

• Proficiency with Kubernetes clusters and distributed compute assets

• Strong communication and teamwork skills

• Readiness to explore and promote cutting edge technologies in ML Infrastructure domain and beyond

Bonus Points

• Demonstrated publications in any of the major conferences

• Multi-node and multi-GPU training

• Mathematical Reasoning - discrete math and logic

• Formal Verification - lean
Show full description

Qualifications
• Experienced Leadership: Our founding team consists of second and third-time entrepreneurs, each with over 25 years of experience in the cybersecurity industry
• This role requires deep technical expertise, a passion for solving complex problems, and a proactive attitude
• Minimum of 5 years experience as a Software Developer with substantial expertise in Golang
• Proven experience in designing and developing microservices architectures and Data Engineering
• Strong background in cloud technologies, preferably Azure
• Experience with Docker, Kubernetes, and CI/CD tools like Jenkins or Spinnaker
• Familiarity with REST APIs, gRPC, and modern database technologies
• Excellent problem-solving skills and ability to work in a team-oriented environment
• 5 more items(s)
Benefits
• Generous Compensation: We offer highly competitive salaries, equity options, and a supportive work environment
• Your contributions will be valued and rewarded as we grow together
• Perks and Benefits
• Comprehensive health benefits (medical, dental, and vision)
• Equity options ensure you have a stake in the company’s success
• Access to the latest tools and technologies for AI/ML development
• 3 more items(s)
Responsibilities
• Job Description: As a Senior Golang Developer, you will be at the forefront of developing microservices using Golang within a cloud-based architecture
• You will play a key role in designing, developing, and deploying scalable systems that enhance business operations
• Design and develop robust microservices using Golang
• Deploy and maintain scalable systems on Azure
• Automate CI/CD workflows and ensure high availability and resilience of applications
• Collaborate with cross-functional teams to enhance system efficiency and integrate new features
• Lead the adoption of best code quality, security, and maintainability practices
• 4 more items(s)
More job highlights
Job description
Hiring Golang Developers for an exciting Startup in the SF Bay Area!

Are you passionate and eager to make a significant impact in the cybersecurity space? Join us at our cutting-edge startup in the San Francisco Bay Area, where we assemble a world-class team to tackle some of the most pressing challenges in cybersecurity.

Why Join Us?

• $25M Seed Funding: We are well-funded, with $25 million rai...sed in our seed round, providing the resources to innovate and scale rapidly.

• Proven Early Success with Fortune 500 Customers: We have started partnering with Fortune 500 companies, marking early success and growing trust in our innovative solutions. This highlights the immense potential and reliability of our AI-powered cybersecurity offerings.

• Experienced Leadership: Our founding team consists of second and third-time entrepreneurs, each with over 25 years of experience in the cybersecurity industry. Their proven expertise and vision drive our ambitious goals, positioning us to lead in AI-powered cybersecurity.

• World-Class Leadership Team: Our Heads of AI, Engineering, and Product bring extensive experience from some of the world’s most influential companies, ensuring top-tier mentorship, direction, and vision.

• Cutting-Edge AI Solutions: Our team leverages the most advanced AI technologies, including Large Language Models (LLMs) and Generative AI.

• Generous Compensation: We offer highly competitive salaries, equity options, and a supportive work environment. Your contributions will be valued and rewarded as we grow together.

• Cybersecurity Knowledge Preferred but Not Required: While experience in cybersecurity is a plus, we are primarily seeking top-tier talent in microservices architecture, software development, and/or DevOps who are passionate about solving complex problems.

About the Role:

Job Description: As a Senior Golang Developer, you will be at the forefront of developing microservices using Golang within a cloud-based architecture. You will play a key role in designing, developing, and deploying scalable systems that enhance business operations. This role requires deep technical expertise, a passion for solving complex problems, and a proactive attitude.

Responsibilities:

• Design and develop robust microservices using Golang.

• Deploy and maintain scalable systems on Azure.

• Automate CI/CD workflows and ensure high availability and resilience of applications.

• Collaborate with cross-functional teams to enhance system efficiency and integrate new features.

• Lead the adoption of best code quality, security, and maintainability practices.

Qualifications:

• Minimum of 5 years experience as a Software Developer with substantial expertise in Golang.

• Proven experience in designing and developing microservices architectures and Data Engineering.

• Strong background in cloud technologies, preferably Azure.

• Experience with Docker, Kubernetes, and CI/CD tools like Jenkins or Spinnaker.

• Familiarity with REST APIs, gRPC, and modern database technologies.

• Excellent problem-solving skills and ability to work in a team-oriented environment.

Our Culture and Team

• Collaborative Environment: You’ll join a dynamic, fast-paced startup where innovation thrives and every team member's voice is valued.

• World-Class Leadership: Our Heads of AI, Engineering, and Product bring extensive experience from some of the world’s best and most influential companies, ensuring top-tier mentorship and strategic direction.

• Growth Opportunities: We support your professional development through mentorship, access to industry conferences, and opportunities to work on cutting-edge AI projects that make a global impact.

• Diversity and Inclusion: We are committed to building a diverse and inclusive team that brings a variety of perspectives to solving today’s cybersecurity challenges.

Work Location:

Our office is in the Silicon Valley Center in North San Jose, CA, providing a collaborative environment where innovation thrives.

Perks and Benefits

• Comprehensive health benefits (medical, dental, and vision).

• Equity options ensure you have a stake in the company’s success.

• Access to the latest tools and technologies for AI/ML development.

Ready to join us on this groundbreaking journey? Apply today to become part of our mission to revolutionize cybersecurity with AI!

#Golang #Software Development #Azure #microservices #kubernetes #Cybersecurity #Startup #Hiring #SanFranciscoBayArea #Innovation #TechJobs
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About Glean:

Founded in 2019, Glean is an innovative AI-powered knowledge management platform designed to help organizations quickly find, organize, and share information across their teams. By integrating seamlessly with tools like Google Drive, Slack, and Microsoft Teams, Glean ensures employees can access the right knowledge at the right time, boosting productivity and collaboration. The compa...ny’s cutting-edge AI technology simplifies knowledge discovery, making it faster and more efficient for teams to leverage their collective intelligence.

Glean was born from Founder & CEO Arvind Jain’s deep understanding of the challenges employees face in finding and understanding information at work. Seeing firsthand how fragmented knowledge and sprawling SaaS tools made it difficult to stay productive, he set out to build a better way - an AI-powered enterprise search platform that helps people quickly and intuitively access the information they need. Since then, Glean has evolved into the leading Work AI platform, combining enterprise-grade search, an AI assistant, and powerful application- and agent-building capabilities to fundamentally redefine how employees work.

About the Role:

Glean is seeking a talented AI Outcomes Manager to join our rapidly expanding team. The AI Outcomes Manager will play a crucial role in transforming how every department works, with the power of Glean. They will work closely with executives and end users, combining business acumen, product sense, and prompting skills to help them transform into an AI-native enterprise.

You will:

• Partner with executive sponsors and end users to identify high‑impact use cases and turn them into measurable business outcomes on Glean.

• Lead strategic reviews and advise customers on their AI roadmap, ensuring they get the most value from Glean’s platform.

• Translate business needs into clear problem statements, success metrics, and practical AI solutions; collaborate with Product and R&D to shape priorities.

• Conduct discovery workshops, scope pilots, and guide rollouts, driving breadth and depth of adoption of the Glean platform.

• Design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability.

• Proactively identify expansion opportunities and drive engagement across teams and functions.

About you:

• 5+ years of professional experience in roles that blend business and technology (e.g., product, analytics, data, engineering, solutions), with a consultative, customer‑facing approach.

• Strong problem‑solving and communication skills; comfortable working with stakeholders from ICs to executives and tailoring messages to different audiences.

• Demonstrated ability to craft effective prompts and guide AI agents for real customer or business workflows; you’ve shipped outcomes, not just demos.

• Understanding of what current LLMs can and cannot do; able to set expectations and steer towards reliable, safe, cost‑effective solutions.

• Product sense and user empathy—you can spot high‑value applications of AI across diverse job functions and design clear, guided experiences.

• Hands‑on experience with modern AI platforms and tools (e.g., OpenAI, Claude, Mistral, Cohere or similar), with enough technical depth to work with engineers without needing to be on the critical path for writing production code.

• Good to have:

• Prior experience in customer‑facing, consultative roles (solutions, support, product management) and comfort presenting to senior leaders.

• Exposure to evaluating AI outcomes (e.g., defining success criteria, using sample tasks, reviewing results) and iterating for quality, latency, and cost.

• Ability to analyze usage signals and customer feedback to inform roadmap and drive adoption.

Location:

• This role is hybrid (4 days a week in our SF Bay Area offices)

Compensation & Benefits:

The standard OTE range for this position is $150,000-$212,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.

We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused.

We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.

#LI-HYBRID
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This a Full Remote job, the offer is available from: Nigeria

Job Overview:

About the role: We’re hiring an AI Prompt Engineer who knows how to turn AI into real operational leverage.

​​​​​This is not a “casual AI user” role. We need someone who understands how to engineer prompts, refine outputs, and build repeatable AI workflows that save time and improve quality across the company.If you love e...xperimenting with AI and building systems that make teams faster, you’ll thrive here.

Key Responsibilities:

• Writing and optimizing prompts primarily in Perplexity (our main AI tool)

• Using Claude and Gemini as supporting tools when needed

• Turning AI outputs into structured, usable results

• Identifying tasks that can be improved or automated with AI

• Building repeatable AI workflows

• Creating SOPs and Loom videos so others can follow your systems

• Continuously improving prompt quality and output reliability

Required Experience & Skills:

• Strong hands-on experience with Perplexity (this is a must)

• Solid understanding of how to write effective AI prompts

• Experience using Claude and/or Gemini

• Excellent written English

• Systems thinker — you like turning chaos into process

• Comfortable working independently in a fast-paced environment

• Experience creating SOPs or documentation is a big plus

Nice to have:

• Experience building AI-assisted automations

• Startup or high-growth company experience

• Experience documenting processes and creating internal training

• Basic understanding of operations, marketing, or outbound workflows

This offer from "Talent Hackers" has been enriched by Jobgether.com and got a 72% flex score.
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Veeam, the #1 global market leader in data resilience, believes businesses should control all their data whenever and wherever they need it. Veeam provides data resilience through data backup, data recovery, data portability, data security, and data intelligence. Based in Seattle, Veeam protects over 550,000 customers worldwide who trust Veeam to keep their businesses running. Join us as we move f...orward together, growing, learning, and making a real impact for some of the world’s biggest brands. The future of data resilience is here - go fearlessly forward with us.

About the Role:

Veeam, following its acquisition of Securiti AI - the leader in AI-powered data security posture management (DSPM) - is seeking experienced Sales Specialists to drive growth in this innovative business.

In this role, you will focus exclusively on Securiti AI solutions, working closely with Veeam account executives to cross-sell into Veeam’s customer base, win new business, and expand existing Securiti AI accounts. You’ll have a shared territory with multiple AEs, strong earning potential, and support from a dedicated Securiti AI solution engineer for technical engagements. You’ll also benefit from Veeam’s robust go-to-market resources to ensure customer success.

What You’ll Do:

• Develop and execute a territory plan to drive repeatable revenue within assigned accounts.

• Collaborate with account executives to identify and maximize cross-sell opportunities for Securiti AI solutions.

• Lead the entire sales process, serving as a trusted advisor, and presenting compelling business cases to customers.

• Manage a pipeline of high-value opportunities, ensuring accurate forecasting and CRM discipline.

• Expand relationships with key channel partners and resellers to accelerate deal flow and market reach.

What You’ll Bring:

• Proven success closing complex enterprise software deals, with 10-15+ years in security software sales.

• Extensive experience managing multithreaded sales cycles involving multiple stakeholders.

• Strong existing relationships with C-suite executives and channel partners in large enterprises.

• Deep understanding of enterprise software sales, particularly in data security, management, or cybersecurity.

• Reputation for integrity, honesty, and building long-term customer relationships.

What You’ll Get

Veeam offers benefits that support your whole self:

• 24 paid volunteer hours annually through Veeam Cares, plus 4 extra global VeeaMe Days for self-care

• Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops and learning events like our annual Global Day of Learning

Please note: If an applicant is permanently located outside of Saudi Arabia, Veeam reserves the right to decline the application for this position

#LI-NT1 #LI-REMOTE

Veeam Software is an equal opportunity employer and does not tolerate discrimination in any form on the basis of race, color, religion, gender, age, national origin, citizenship, disability, veteran status or any other classification protected by federal, state or local law. All your information will be kept confidential.

Please note that any personal data collected from you during the recruitment process will be processed in accordance with our Recruiting Privacy Notice.

The Privacy Notice sets out the basis on which the personal data collected from you, or that you provide to us, will be processed by us in connection with our recruitment processes.

By applying for this position, you consent to the processing of your personal data in accordance with our Recruiting Privacy Notice.

By submitting your application, you acknowledge that the information provided in your job application and any supporting documents is complete and accurate to the best of your knowledge. Any misrepresentation, omission, or falsification of information may result in disqualification from consideration for employment or, if discovered after employment begins, termination of employment.
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About Us At SkyLabs AI Inc.

, we are at the forefront of the artificial intelligence revolution.

As a US-headquartered company, we conduct applied research on AI for intelligent reasoning.

We specialize in complex neurosymbolic AI to solve intricate problems within software engineering.

Our team is composed of world-class researchers and engineers dedicated to building the platforms and intelligen...t agents that will power the next generation of software.

If you are passionate about building truly intelligent systems and want to make a lasting impact, join us.

The Role We are seeking an exceptional Senior AI Research Engineer with a strong focus on training and improving LLMs across the full lifecycle —from Domain‑Adaptive Pretraining to Supervised Fine‑Tuning (SFT) , RL/RLVR , and advanced post‑training techniques (e.

g., reward modeling, preference optimization, RL‑VR style workflows).

You will lead hands‑on training efforts, contribute to research direction, and build robust pipelines for data, training, evaluation, and deployment.

You should have in‑depth understanding of LLM internals (attention/MLP dynamics, normalization, optimization behavior, scaling laws), and modern architectures including Mixture of Experts (MoE) and other frontier design choices.

You will work closely with product and platform teams to translate research ideas into performant systems.

Salaries in USD (income tax exemptions) Work in Pakistan Timezone Comprehensive health allowance Monthly team events and activities Relocation allowance (if you're moving to Islamabad) Opportunity to work with top minds in the industry and academia Startup culture where ideas are heard at all levels Adequate annual, sick, casual and parental leaves Key Responsibilities Lead the team of AI Engineers/Researchers Train and iterate on LLMs end-to-end across DAPT/CPT, SFT, and RL-based post-training (preference optimization, reward modeling, verifiable rewards, policy optimization, and related variants).

Design training recipes : tokenization strategy, curriculum/sampling, batch/sequence packing, optimizer + scheduler choices, stability techniques, and hyperparameter search.

Architect and implement efficient training systems using distributed training (data/tensor/pipeline parallelism), FSDP/ZeRO, activation checkpointing, mixed precision, and throughput optimization.

Develop and maintain LLM data pipelines : large-scale data ingestion, filtering, deduplication, contamination checks, domain balancing, safety filtering, and dataset versioning.

Perform deep EDA and root-cause analysis for training/eval regressions (loss spikes, instability, alignment drift, memorization, toxicity, domain overfitting), and implement mitigations.

Build evaluation and benchmarking suites : offline metrics, human preference pipelines, automatic evals, domain-specific test sets, adversarial probing, and model behavior tracking over time.

Apply and advance model compression and efficiency : distillation from LLMs, quantization-aware considerations, pruning/sparsity (including MoE routing behavior), and inference-time optimization tradeoffs.

Implement and operationalize LLMOps : experiment tracking, reproducible runs, model registry, training telemetry, artifact management, CI for training configs, and safe rollout strategies.

Leverage AI coding tools (e.

g., Cursor, Copilot) to accelerate development while maintaining strong engineering rigor, testability, and code review standards.

Collaborate cross-functionally with platform, infra, and product teams to integrate trained models into agentic/production pipelines (retrieval, tool use, evaluation gates, monitoring).

Stay current with the field and translate new ideas (architectures, post-training, data strategies, evals) into internal experiments and improvements.

Qualifications & Skills 5+ years in ML engineering / applied research, with significant hands-on experience training large models , especially LLMs.

Strong practical knowledge of transformer internals and modern variations (MoE, routing, load balancing, attention variants, normalization choices, long-context strategies).

Demonstrated experience with DAPT/CPT , SFT , and RL-based post-training (e.

g., reward modeling, preference datasets, verifiable rewards, policy optimization).

Ability to reason about why a method works and when it fails.

Excellent understanding of data science fundamentals : EDA, dataset design, bias/contamination, sampling strategies, and error analysis.

Proven ability to root-cause and debug training instabilities and performance regressions (numerics, data issues, infra bottlenecks, config drift).

Expert-level programming in Python , with strong software engineering fundamentals (data structures, algorithms, systems thinking).

Experience with key tooling such as PyTorch , distributed training frameworks (FSDP/DeepSpeed/ZeRO), and training orchestration.

Familiarity with LLMOps : tracking (e.

g., W&B/MLflow), dataset versioning, reproducibility, deployment constraints, and monitoring of model behavior.

Comfortable building agentic pipelines around LLMs (evaluation agents, data generation/labeling loops, model-in-the-loop distillation, automated red-teaming).

Bonus: publications, open-source contributions, or demonstrated leadership in shipping LLM training improvements.

Who You Are You’re a pragmatic researcher-engineer: you can read papers, run experiments, and also build the training and data systems that make results reproducible and shippable.

You’re meticulous about data and measurement, fast at debugging, and comfortable making tradeoffs between quality, cost, and timeline.

You communicate clearly, mentor others, and thrive in an environment where you’re trusted to drive big initiatives.
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Qualifications
• People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack
• The closer you are to fullstack (inference + posttraining/RL + systems), the stronger the fit-but being spiky in one area and eager to grow is absolutely okay
• Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others:
• Systemsfirst profile: Largescale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving
• RLfirst profile: RL / posttraining for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPOlike methods, reward modeling), and using these to train or finetune real models
• Model architecture design for Transformers or other large neural nets
• Distributed systems / highperformance computing for ML
• Are comfortable working from algorithms to engines:
• Strong coding ability in Python
• Experience profiling and optimizing performance across GPU, networking, and memory layers
• Track record of impactful work in ML systems, RL, or largescale model training (papers, opensource projects, or production systems)
• Can read new RL / posttraining papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API)
• Operate well as a fullstack problem solver:
• You naturally ask: "Where in the stack is this really bottlenecked?"
• You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and uservisible wins
• 3+ years of experience working on ML systems, largescale model training, inference, or adjacent areas (or equivalent experience via research / open source)
• Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience
• Demonstrated experience owning complex technical projects endtoend
• 15 more items(s)
Benefits
• We offer competitive compensation, startup equity, health insurance and other competitive benefits
• The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits
• Our salary ranges are determined by location, level and role
Responsibilities
• Our mandate is to push the frontier of efficient inference and RLdriven training: making models dramatically faster and cheaper to run, while improving their capabilities through RLbased posttraining (e.g., GRPOstyle objectives)
• This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack
• Much of the job is modifying production inference systems-for example, SGLang or vLLMstyle serving stacks and speculative decoding systems such as ATLAS-grounded in a strong understanding of posttraining and inference theory, rather than purely theoretical algorithm design
• You'll work across the stack-from RL algorithms and training engines to kernels and serving systems-to build and improve frontier models via RL pipelines
• Depth in one of these areas plus appetite to collaborate across (and grow toward more fullstack ownership over time) is ideal
• Able to take a new sampling method, scheduler, or RL update and turn it into a productiongrade implementation in the engine and/or training stack
• Have a solid research foundation in your area(s) of depth:
• Advance inference efficiency endtoend
• Design and prototype algorithms, architectures, and scheduling strategies for lowlatency, highthroughput inference
• Implement and maintain changes in highperformance inference engines (e.g., SGLang or vLLMstyle systems and Together's inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc
• Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost
• Unify inference with RL / posttraining
• Design and operate RL and posttraining pipelines (e.g., RLHF, RLAIF, GRPO, DPOstyle methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems
• Make RL and posttraining workloads more efficient with inferenceaware training loops-for example, async RL rollouts, speculative decoding, and other techniques that make largescale rollout collection and evaluation cheaper
• Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack
• Codesign algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and userfacing layers
• Run ablations and scaleup experiments to understand tradeoffs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design
• Own critical systems at production scale
• Profile, debug, and optimize inference and posttraining services under real production workloads
• Drive roadmap items that require real engine modification-changing kernels, memory layouts, scheduling logic, and APIs as needed
• Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously
• Provide technical leadership (Staff level)
• Set technical direction for crossteam efforts at the intersection of inference, RL, and posttraining
• Mentor other engineers and researchers on fullstack ML systems work and performance engineering
• 21 more items(s)
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Job description
About the Role

The Turbo team sits at the intersection of efficient inference (algorithms, architectures, engines) and posttraining / RL systems. We build and operate the systems behind Together's API, including highperformance inference and RL/posttraining engines that can run at production scale.

Our mandate is to push the frontier of efficient inference and RLdriven training: making models dr...amatically faster and cheaper to run, while improving their capabilities through RLbased posttraining (e.g., GRPOstyle objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems-for example, SGLang or vLLMstyle serving stacks and speculative decoding systems such as ATLAS-grounded in a strong understanding of posttraining and inference theory, rather than purely theoretical algorithm design.

You'll work across the stack-from RL algorithms and training engines to kernels and serving systems-to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RLfirst, some are more systemsfirst. Depth in one of these areas plus appetite to collaborate across (and grow toward more fullstack ownership over time) is ideal.

Requirements

We don't expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to fullstack (inference + posttraining/RL + systems), the stronger the fit-but being spiky in one area and eager to grow is absolutely okay.

You might be a good fit if you:

• Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others:

• Systemsfirst profile: Largescale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving.

• RLfirst profile: RL / posttraining for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPOlike methods, reward modeling), and using these to train or finetune real models.

• Model architecture design for Transformers or other large neural nets.

• Distributed systems / highperformance computing for ML.

• Are comfortable working from algorithms to engines:

• Strong coding ability in Python

• Experience profiling and optimizing performance across GPU, networking, and memory layers.

• Able to take a new sampling method, scheduler, or RL update and turn it into a productiongrade implementation in the engine and/or training stack.

• Have a solid research foundation in your area(s) of depth:

• Track record of impactful work in ML systems, RL, or largescale model training (papers, opensource projects, or production systems).

• Can read new RL / posttraining papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API).

• Operate well as a fullstack problem solver:

• You naturally ask: "Where in the stack is this really bottlenecked?"

• You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and uservisible wins.

Minimum qualifications

• 3+ years of experience working on ML systems, largescale model training, inference, or adjacent areas (or equivalent experience via research / open source).

• Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience.

• Demonstrated experience owning complex technical projects endtoend.

If you're excited about the role and strong in some of these areas, we encourage you to apply even if you don't meet every single requirement.

Responsibilities

• Advance inference efficiency endtoend

• Design and prototype algorithms, architectures, and scheduling strategies for lowlatency, highthroughput inference.

• Implement and maintain changes in highperformance inference engines (e.g., SGLang or vLLMstyle systems and Together's inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc.

• Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost.

• Unify inference with RL / posttraining

• Design and operate RL and posttraining pipelines (e.g., RLHF, RLAIF, GRPO, DPOstyle methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems.

• Make RL and posttraining workloads more efficient with inferenceaware training loops-for example, async RL rollouts, speculative decoding, and other techniques that make largescale rollout collection and evaluation cheaper.

• Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack.

• Codesign algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and userfacing layers.

• Run ablations and scaleup experiments to understand tradeoffs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design.

• Own critical systems at production scale

• Profile, debug, and optimize inference and posttraining services under real production workloads.

• Drive roadmap items that require real engine modification-changing kernels, memory layouts, scheduling logic, and APIs as needed.

• Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously.

• Provide technical leadership (Staff level)

• Set technical direction for crossteam efforts at the intersection of inference, RL, and posttraining.

• Mentor other engineers and researchers on fullstack ML systems work and performance engineering.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our privacy policy at https://www.together.ai/privacy
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About the Role

We are hiring an AI AgentSpecialist to design, build, and optimize AI agents using an agentic AI platform. This role focuses on solving real logistics workflow problems by combining strong prompt engineering skills, practical understanding of APIs and tools, and the ability to translate business logic into reliable agent behavior.

You will work closely with IT, product, developers..., and operations teams to deliver agents that reduce manual workload, improve response quality, and unlock new automation opportunities for the business.

Key Responsibilities

AI Agent Development

• Design, develop, andoptimizeAI agents using agentic platforms to solve real-world business problems.

• Build conversational AI workflows and multi-step agent systems that integrate with existing tools and APIs.

• Create and continuously refine prompts and system instructions to improve agent performance, reliability, and user experience.

• Useworkflowbuilders, tools, and functions to modellogisticsprocesses such as booking, quoting, track & trace, documentation, and customer inquiries.

Prompt Engineering

• Apply advanced prompt engineering principles, including few-shot prompting, structured outputs, and controlled reasoning.

• Develop reusable prompting patterns and system templates for consistency across agents.

• Maintain a prompt library and ensure alignment with internal standards.

QA test

• Conductteststo evaluate agent performance,identifyareas for improvement in reasoning, accuracy, and task completion.

• Design effective evaluation frameworks and test scenarios based on real customer and operational questions.

• Troubleshoot failures and refine logic, tools, and prompts to improve reliability.

Collaboration

• Work with product and engineering teams to prototype and iterate on AI-powered features.

• Collaborate withcross-functionalteams to understand real workflows and business needs andtranslate them intonew agentic AI use cases.

• Support onboarding, rollout, and continuous improvement of agents.

Documentation and Knowledge Sharing

• Document prompting patterns, best practices, agent architectures, and integration methods.

• Maintain version control for prompt updates, workflows, and tool integrations.

• Share insights with the wider team to raise the overall AI capability across the company.

Requirements

Experience

• Minimum5years of experience working across digital automation andgenerativeAI projects in a professional or project capacity.

• Hands-on experience building AI agents or conversational AI applications.

• Experience inlogisticsor supply chain operations is strongly preferred, especially areas like warehouse management, network planning, or last-mile delivery.

Technical Skills

• Strong understanding of prompt engineering principles including few-shot learning, chain-of-thought reasoning (implicit), and structured output design.

• Familiarity with the modern AI stack and clear understanding of LLM capabilities and limitations.

• Ability to work with REST APIs, JSON, and simple scripting to integrate tools and external systems.

• Ability to design and run evaluation frameworks for agent performance.

Soft Skills

• Clear, structured thinking with a problem-solving mindset.

• Eagernessand motivation to keep up withnew trendsin AI and be at the frontier ofnew technologiesand AI-driven opportunities

• Ability to work closely with both technical and non-technical stakeholders.

• High accountability and comfort iterating quickly.

• Strong communicationskills and attention to detail.
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This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Researcher in Nigeria.

This role offers a unique opportunity to influence the foundation of AI model performance by focusing on the quality and structure of training data. You will lead efforts to curate, assess, and optimize large-scale, unstructured datasets that power sta...te-of-the-art AI systems. Working closely with research and engineering teams, you will apply statistical, computational, and ML-driven techniques to improve dataset diversity, representativeness, and overall impact. The position requires a highly analytical and independent thinker who can design frameworks to evaluate and de-risk datasets while contributing to the development of automated data preprocessing and validation tools. This is a highly collaborative role in a fast-moving, high-trust environment that encourages ownership, experimentation, and measurable impact on real-world AI outcomes.

Accountabilities:

• Lead the evaluation, curation, and optimization of large-scale unstructured datasets for AI model training.

• Design and implement statistical and machine learning methods to assess data quality, diversity, and informativeness.

• Collaborate with model training teams to identify data bottlenecks and optimize dataset performance.

• Provide leadership on data quality strategy and establish best practices for dataset assessment.

• Evaluate external datasets for scalability, relevance, and integration, creating data scorecards as needed.

• Contribute to R&D of tools that automate data preprocessing, validation, and enhancement processes.

• Communicate findings and improvements to research, engineering, and cross-functional teams.

Requirements:

• PhD or equivalent Master’s degree with 4+ years of industry experience in machine learning, computer science, statistics, mathematics, engineering, or a related quantitative field.

• Strong understanding of AI training pipelines, including preprocessing, evaluation, and optimization of datasets.

• Experience handling large, unstructured datasets, particularly in text-based domains.

• Background in statistical analysis, bias detection, and data validation methodologies.

• Ability to identify high-impact problems and independently develop solutions.

• Excellent collaboration and communication skills, with experience working across technical teams.

• Bonus: experience with synthetic data generation, dataset augmentation, or development of evaluation frameworks; publications or open-source contributions in data-centric AI.

Benefits:

• Competitive compensation and performance-based incentives.

• Fully remote, flexible working environment.

• Opportunity to work on high-impact AI projects with access to large-scale datasets.

• Collaborative and high-trust culture emphasizing ownership and velocity.

• Direct influence on AI model performance and data strategy.

• Exposure to cutting-edge AI research and applied machine learning projects.

Why Apply Through Jobgether?

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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Position Overview

• We are seeking a highly experienced AI Automation & Strategy Lead to drive the company’s end-to-end artificial intelligence, automation, and data strategy.

• This individual will architect and implement agentic and sub-agent workflows across the organization, build AI-driven systems to improve operational efficiency and reporting, and lead adoption of AI tools and best practice...s company-wide.

• This role combines strategy, systems architecture, and hands-on implementation. The successful candidate will play a central role in building our internal technology capabilities and shaping the future of how Herbal Goodness operates.

Key Responsibilities

AI & Automation Strategy:

• Develop and lead the company’s AI and automation roadmap aligned with business goals.

• Identify opportunities to streamline workflows, improve decision-making, and reduce operational friction through AI.

• Drive adoption of AI tools and workflows across departments, including training and change management.

• Establish AI standards, governance frameworks, and playbooks across the organization.

Workflow Design & Implementation:

• Design and implement agentic and sub-agent workflows across key business processes.

• Build and maintain automations using platforms such as n8n, Make, Zapier, and Claude Code.

• Lead business process mapping and redesign initiatives to ensure workflows are optimized prior to automation.

AI Systems, ERP & Reporting:

• Architect and build AI-driven ERP and operational systems to support workflow automation, reporting, and analytics.

• Create dashboards and reporting systems to support executive and operational decision-making.

• Build internal dashboards and reporting pipelines using Looker Studio and related tools.

Data, Databases Tools & Analytics

• Design and manage structured databases, data environments and reporting pipelines.

• Work with tools and systems including:

• Notion databases

• Snowflake

• Google BigQuery

• Looker Studio

• Develop analytics models and forecasting tools where appropriate.

AI & Agent Infrastructure:

• Build and maintain advanced AI workflows using:

• LangChain or LlamaIndex

• Retrieval-Augmented Generation RAG pipelines

• Vector databases such as Pinecone, Weaviate, or Supabase

Automation & Integration:

• Lead API integrations between internal and external systems.

• Implement automations using:

• Webhooks and REST architectures

• Python scripting for automation

• Workflow orchestration tools

Infrastructure & Deployment:

• Support containerized and scalable deployments using Docker or similar tools.

• Evaluate infrastructure requirements for reliability and scalability of internal tools.

Governance, Security & Ethics:

• Ensure data integrity, security, and responsible use of AI.

• Implement access controls, auditability, and accountability in automated systems.

Strategic Leadership Responsibilities:

• Establish AI standards and operational playbooks across the organization.

• Evaluate vendors and select best-in-class tools and platforms.

• Build internal knowledge by training teams and enabling adoption.

• Develop and maintain a long-term data architecture strategy.

Qualifications

Required Qualifications:

• Significant experience in AI automation, workflow orchestration, or systems engineering.

• Deep expertise in API integrations and automation tools.

• Experience building production-grade automation or data systems.

• Strong knowledge of data analytics, reporting, and dashboards.

• Experience evaluating and implementing emerging AI technologies.

• Excellent problem-solving, communication, and systems-thinking skills.

Preferred Qualifications:

• Experience building AI systems, Agentic workflows, ERP, or internal operational systems.

• Experience building reporting dashboards that use Ai for data analytics and synthesis.

• Background in supply chain, manufacturing, or CPG operations is a plus.

• Experience implementing AI governance or data governance frameworks.

Key Attributes:

• Strategic thinker with strong execution capability.

• Highly organized and process-driven.

• Curious, proactive, and forward-looking.

• Strong ownership mindset and accountability.

Primary Initial Goals:

• Build agentic tools to support core workflows.

• Map and redesign key company processes.

• Develop executive dashboards and operational reporting systems.
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Remote Medical Expert - AI Trainer

Qualifications

Benefits

Responsibilities

Full Description

Benefits

Zero to AI Engineer