Data Scientist with Agentic AI

Job highlights Identified by Google from the original job post Qualifications • FULL-STACK PRODUCT ENGINEERING • CLIENT-FACING + QA • 5+ years of software engineering experience in fast-moving, high-performance environments • Strong full-stack engineering experience - you have shipped real products end-to-end, not just maintained existing ones • Comfortable operating in client-facing settings • With an ability to clearly explain technical architecture, AI methodology, and product decisions to non-technical stakeholders with confidence • Deep hands-on experience with Neo4j and graph data modeling • Practical experience integrating LLMs and building production-grade API integrations with AI platforms • Ability to design systems that avoid constant LLM calls and manage cost, latency, and quality tradeoffs • Proficient in React, TypeScript, Node.js, and modern frontend and backend frameworks • Experience designing and working with scalable databases and APIs • Clear, confident communicator who is energized by client interaction - not just tolerant of it • With the ability to lead technical demos, respond to live client questions, and translate complex systems into clear business value narratives • Experience with applied data science, ML-adjacent systems, or experimentation frameworks • Background in AI search, recommendation systems, SEO-like ranking models, or similar • Practical agent orchestration experience - deterministic, evaluable, production flows • Familiarity with conversational commerce or AI-powered transactional flows • Experience working on creative or content-driven platforms • You move fast and finish things • You are comfortable owning a feature from whiteboard to production, and you do not need someone to hand you a spec • You bring strong opinions about how to build things well and you are willing to defend them in a conversation, then document them afterward • In a given week you might be extending the knowledge graph, debugging an LLM orchestration flow, presenting analysis to an enterprise client, and shipping a frontend improvement • You want to be close to the work, close to the product, and close to the client • 20 more items(s) Responsibilities • We’re looking for a Full-Stack Engineer, AI Product & Client Solutions who can truly own and ship product end-to-end across frontend, backend, and data layers without needing constant oversight • You’ll be responsible for architecting and delivering systems that power our Brand Knowledge Graph, building interfaces that make complex enterprise data intuitive, and developing the LLM orchestration layer so it runs efficiently and reliably at scale • Equally important, you’re comfortable operating autonomously and taking full ownership from concept through deployment • You can build it, explain it, demo it, and defend the technical decisions behind it, translating complex systems into clear narratives for clients without needing to be managed step-by-step • BRAND KNOWLEDGE GRAPH + DATA SYSTEMS • Develop and extend our Brand Knowledge Graph in Neo4j: modeling how changes to one node propagate and affect AI visibility and recommendations elsewhere • Build predictive optimization systems that model how specific content and strategy changes impact AI visibility outcomes • Design measurement and feedback loops that connect changes to outcomes and track predicted vs. actual lift over time • Implement scoring logic including Share-of-Prompt, accuracy measurement, sentiment analysis, and competitor mention detection • Analyze how optimizations at scale affect long-term LLM behavior and ecosystem dynamics • LLM ORCHESTRATION + AI INTEGRATIONS • Build and maintain integrations with LLM platforms including ChatGPT, Claude, Grok, Perplexity, DeepSeek, and others as they emerge • Design orchestration systems that minimize unnecessary LLM calls: managing cost, latency, and quality tradeoffs intelligently • Develop agent-based analysis workflows that evaluate multiple optimization scenarios in parallel and forecast impact • Compare and stress-test multiple optimization strategies simultaneously to surface the most effective approaches • Build and ship full-stack features across frontend, backend, and data layers: from idea to production • Develop high-quality frontend interfaces in React and TypeScript that translate complex graph and model outputs into actionable insights for users • Design and optimize backend systems for performance, security, and scalability • Build workflow and tracking infrastructure recording what changed, why it changed, and the outcome - integrating with Jira, Slack, HubSpot, and email • Lay foundations for AI-native commerce experiences where merchants can transact directly inside AI chat • Work closely with design and product to translate ideas into polished, production-ready experiences • Participate actively in client calls, demos, and technical conversations - explaining systems clearly to non-technical enterprise stakeholders • Own QA processes and fix pack delivery: building test coverage, triaging bugs, and maintaining data integrity across the platform • Pull insights from complex datasets and translate them into findings that clients and internal teams can act on • Document architecture, decisions, and systems to support a growing team and future CTO onboarding • 22 more items(s) More job highlights Job description ABOUT US

Emberos exists to bring predictive intelligence and clarity to a world where AI is already shaping decisions.

We help brands understand how AI interprets and represents them at the moment of discovery and how that understanding compounds over time.

By revealing emerging patterns before assumptions harden into belief, we give leaders foresight into what is taking shape and the ability to... act early.

This is not just monitoring.

It’s predictive intelligence designed for an AI-mediated world.

As AI increasingly defines what people see, trust, and choose, Emberos serves as a grounding force for truth by bringing clarity to how meaning forms today and how it evolves tomorrow.

We’re a small, fast-moving team building something foundational, and we’re looking for people who want to shape what comes next.

The Opportunity

We’re looking for a Full-Stack Engineer, AI Product & Client Solutions who can truly own and ship product end-to-end across frontend, backend, and data layers without needing constant oversight.

You’ll be responsible for architecting and delivering systems that power our Brand Knowledge Graph, building interfaces that make complex enterprise data intuitive, and developing the LLM orchestration layer so it runs efficiently and reliably at scale.

Equally important, you’re comfortable operating autonomously and taking full ownership from concept through deployment.

You can build it, explain it, demo it, and defend the technical decisions behind it, translating complex systems into clear narratives for clients without needing to be managed step-by-step.

What You'll Build

BRAND KNOWLEDGE GRAPH + DATA SYSTEMS

• Develop and extend our Brand Knowledge Graph in Neo4j: modeling how changes to one node propagate and affect AI visibility and recommendations elsewhere

• Build predictive optimization systems that model how specific content and strategy changes impact AI visibility outcomes

• Design measurement and feedback loops that connect changes to outcomes and track predicted vs. actual lift over time

• Implement scoring logic including Share-of-Prompt, accuracy measurement, sentiment analysis, and competitor mention detection

• Analyze how optimizations at scale affect long-term LLM behavior and ecosystem dynamics

LLM ORCHESTRATION + AI INTEGRATIONS

• Build and maintain integrations with LLM platforms including ChatGPT, Claude, Grok, Perplexity, DeepSeek, and others as they emerge

• Design orchestration systems that minimize unnecessary LLM calls: managing cost, latency, and quality tradeoffs intelligently

• Develop agent-based analysis workflows that evaluate multiple optimization scenarios in parallel and forecast impact

• Compare and stress-test multiple optimization strategies simultaneously to surface the most effective approaches

FULL-STACK PRODUCT ENGINEERING

• Build and ship full-stack features across frontend, backend, and data layers: from idea to production

• Develop high-quality frontend interfaces in React and TypeScript that translate complex graph and model outputs into actionable insights for users

• Design and optimize backend systems for performance, security, and scalability

• Build workflow and tracking infrastructure recording what changed, why it changed, and the outcome - integrating with Jira, Slack, HubSpot, and email

• Lay foundations for AI-native commerce experiences where merchants can transact directly inside AI chat

• Work closely with design and product to translate ideas into polished, production-ready experiences

CLIENT-FACING + QA

• Participate actively in client calls, demos, and technical conversations - explaining systems clearly to non-technical enterprise stakeholders

• Own QA processes and fix pack delivery: building test coverage, triaging bugs, and maintaining data integrity across the platform

• Pull insights from complex datasets and translate them into findings that clients and internal teams can act on

• Document architecture, decisions, and systems to support a growing team and future CTO onboarding

Must-Have Experience

• 5+ years of software engineering experience in fast-moving, high-performance environments

• Strong full-stack engineering experience - you have shipped real products end-to-end, not just maintained existing ones

• Comfortable operating in client-facing settings. With an ability to clearly explain technical architecture, AI methodology, and product decisions to non-technical stakeholders with confidence.

• Deep hands-on experience with Neo4j and graph data modeling

• Practical experience integrating LLMs and building production-grade API integrations with AI platforms

• Ability to design systems that avoid constant LLM calls and manage cost, latency, and quality tradeoffs

• Proficient in React, TypeScript, Node.js, and modern frontend and backend frameworks

• Experience designing and working with scalable databases and APIs

• Clear, confident communicator who is energized by client interaction - not just tolerant of it.

With the ability to lead technical demos, respond to live client questions, and translate complex systems into clear business value narratives.

Nice to Have

• Experience with applied data science, ML-adjacent systems, or experimentation frameworks

• Background in AI search, recommendation systems, SEO-like ranking models, or similar

• Practical agent orchestration experience - deterministic, evaluable, production flows

• Familiarity with conversational commerce or AI-powered transactional flows

• Experience working on creative or content-driven platforms

Who Thrives Here

You move fast and finish things.

You are comfortable owning a feature from whiteboard to production, and you do not need someone to hand you a spec.

You bring strong opinions about how to build things well and you are willing to defend them in a conversation, then document them afterward.

You get energy from variety.

In a given week you might be extending the knowledge graph, debugging an LLM orchestration flow, presenting analysis to an enterprise client, and shipping a frontend improvement.

You are not looking for a quiet corner of a large eng org.

You want to be close to the work, close to the product, and close to the client.

Emberos is an equal opportunity employer committed to a diverse, equitable and inclusive work environment; dedicated to providing equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, and veteran status.

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