Senior AI/ML Operations & Developer Engineer

Qualifications

• The ideal candidate will have experience building, training, and deploying ML models in real-world environments, while also being fluent in modern data engineering workflows and Dev Ops practices
• The ideal candidate will have experience building, training, and deploying ML models in real-world environments, while also being fluent in modern data engineering workflows and Dev Ops practices
• This role is best suited for individuals who are excited by AI-driven applications, optimization problems, and ML lifecycle automation
• ML Engineering Hands-on experience with building, training, evaluating, and deploying ML/DL models using modern frameworks (e.g., PyTorch, Tensor Flow, Scikit-learn, XGBoost)
• MLOps Foundations Practical understanding of ML lifecycle orchestration using tools such as MLflow, Sage Maker, or custom pipelines
• Data Infrastructure
• Experience with structured and unstructured data ingestion, feature engineering, and pipeline construction using Python, SQL, and cloud-native tools (e.g., AWS Lambda, S3, Dynamo
• Model Deployment Exposure to containerization (Docker), REST API development, and deploying models to production environments
• Version Control Fluency with GIT for collaborative development and code management
• LLMs & GenAI Exposure to large language models (e.g., OpenAI APIs, Hugging Face), prompt engineering, fine-tuning, or RAG pipelines using Lang Chain or similar frameworks
• Statistical & Time-Series Modeling Knowledge of time-series forecasting techniques (ARIMA, LSTM), regression modeling, and statistical inference
• Nice-to-Have Skills and Experience
• Visualization & Dashboards Ability to present model outputs and insights via interactive dashboards (e.g., Plotly Dash, Tableau)
• Reinforcement Learning Familiarity with RL and decision-making systems, especially in constrained environments (e.g., IoT, robotics)
• Dev Ops Practices CI/CD pipelines, infrastructure-as-code, cloud-based automation, and monitoring solutions
• Visualization of Scientific Workflows Experience working with high-dimensional biomedical, industrial, or simulation datasets
• Multimodal Data Fusion Ability to integrate diverse data sources (e.g., imagery, tabular, textual) into a unified ML workflow

Benefits

• Salary Range $156,500-$211,700 per year
• We value people as individuals and our dedication is to reward people competitively and equitably for the work they do and the skills and experience they bring to Arm
• Salary is only one component of Arm's offering
• The total reward package will be shared with candidates during the recruitment and selection process

Responsibilities

• This role is best suited for individuals who are excited by AI-driven applications, optimization problems, and ML lifecycle automation

Full Description

Senior AI/ML Operations & Developer Engineer

Join to apply for the Senior AI/ML Operations & Developer Engineer role at Arm

Senior AI/ML Operations & Developer Engineer

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Join to apply for the Senior AI/ML Operations & Developer Engineer role at Arm

Job Overview

We are seeking a passionate and versatile AI/ML Ops + Developer Engineer who thrives atthe intersection of software engineering, semiconductor engineering, machine learning, and scalable deployment. The ideal candidate will have experience building, training, and deploying ML models in real-world environments, while also being fluent in modern data engineering workflows and Dev Ops practices. This role is best suited for individuals who are excited by AI-driven applications, optimization problems, and ML lifecycle automation.

Job Overview

We are seeking a passionate and versatile AI/ML Ops + Developer Engineer who thrives at the intersection of software engineering, semiconductor engineering, machine learning, and scalable deployment. The ideal candidate will have experience building, training, and deploying ML models in real-world environments, while also being fluent in modern data engineering workflows and Dev Ops practices. This role is best suited for individuals who are excited by AI-driven applications, optimization problems, and ML lifecycle automation.

Required Skills And Experience

• ML Engineering Hands-on experience with building, training, evaluating, and deploying ML/DL models using modern frameworks (e.g., PyTorch, Tensor Flow, Scikit-learn, XGBoost).

• MLOps Foundations Practical understanding of ML lifecycle orchestration using tools such as MLflow, Sage Maker, or custom pipelines.

• Data Infrastructure

Experience with structured and unstructured data ingestion, feature engineering, and pipeline construction using Python, SQL, and cloud-native tools (e.g., AWS Lambda, S3, Dynamo

DB).

• Software Engineering Proficiency in Python; familiarity with R, C++, or Java/Scala is a plus.

• Model Deployment Exposure to containerization (Docker), REST API development, and deploying models to production environments.

• Version Control Fluency with GIT for collaborative development and code management.

• LLMs & GenAI Exposure to large language models (e.g., OpenAI APIs, Hugging Face), prompt engineering, fine-tuning, or RAG pipelines using Lang Chain or similar frameworks.

• Statistical & Time-Series Modeling Knowledge of time-series forecasting techniques (ARIMA, LSTM), regression modeling, and statistical inference.

Nice-to-Have Skills and Experience

• Visualization & Dashboards Ability to present model outputs and insights via interactive dashboards (e.g., Plotly Dash, Tableau).

• Reinforcement Learning Familiarity with RL and decision-making systems, especially in constrained environments (e.g., IoT, robotics).

• Dev Ops Practices CI/CD pipelines, infrastructure-as-code, cloud-based automation, and monitoring solutions.

• Visualization of Scientific Workflows Experience working with high-dimensional biomedical, industrial, or simulation datasets.

• Multimodal Data Fusion Ability to integrate diverse data sources (e.g., imagery, tabular, textual) into a unified ML workflow.

Preferred Background

• BS or MS or PhD in Data Science, Computer Science, Engineering, or related technical field.

• Internship or full-time experience in applied ML roles across industry or research.

• A portfolio of ML applications or publications showing real-world problem-solving capability.

In Return

Arm is committed to global talent acquisition, offering an attractive relocation package. With offices around the world, Arm is a globally diverse organization of dedicated and highly creative engineers. By enabling a dynamic, inclusive, meritocratic, and open workplace, where all our people can grow and succeed, we encourage all to share their unrivaled contributions to Arm's success in the global marketplace.

Salary Range $156,500-$211,700 per year

We value people as individuals and our dedication is to reward people competitively and equitably for the work they do and the skills and experience they bring to Arm. Salary is only one component of Arm's offering. The total reward package will be shared with candidates during the recruitment and selection process.

Accommodations at Arm

At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email . To note, by sending us the requested information, you consent to its use by Arm to arrange for appropriate accommodations. All accommodation or adjustment requests will be treated with confidentiality, and information concerning these requests will only be disclosed as necessary to provide the accommodation.

Although this is not an exhaustive list, examples of support include breaks between interviews, having documents read aloud, or office accessibility. Please email us about anything we can do to accommodate you during the recruitment process.

Hybrid Working at Arm

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