Machine Learning Engineer Role

📋 Job Overview

OPEN Data Jobs (ODJ) is built to craft data careers. We connect high-potential data, AI, and software professionals with meaningful work on the cutting edge of the federal sector, and our aim reaches beyond any single opening: to help you understand your strengths, find the right role, and grow throughout your career.

🏢 About OPEN Data Jobs

ODJ is organized around Roles and Openings. A Role is a durable career type, such as Data Engineer or AI Engineer: it describes the work, the skills that count, and where the path can lead. An Opening is a specific position hiring right now, with a level, a location, and an application. Roles help you see where you fit; from there, there is more than one way to move.

🎯 The Role

Machine Learning Engineers make machine learning and AI models reproducible, deployable, scalable, and supportable. They build the path from training data and experimentation to a versioned model service that can be released, monitored, retrained, and retired without guesswork.

✅ Key Responsibilities

• Reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts.
• Model-serving systems and APIs designed for appropriate latency, throughput, availability, scaling, and rollback.
• Feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls.
• Monitoring and alerting for data quality, drift, model performance, fairness, infrastructure health, latency, and cost.
• Reusable libraries, templates, environments, and delivery pipelines that give data scientists a tested path from experiment to production.

📌 Required Qualifications

• Strong programming and software-engineering practice, including testing, version control, packaging, automation, and production debugging.
• Working knowledge of model development, evaluation metrics, feature engineering, data splitting, tuning, and the limits of different modeling approaches.
• Experience with training and inference pipelines, containers, cloud or on-premises compute, artifact management, and automated deployment.
• Practical MLOps experience with model registries, lineage, reproducibility, monitoring, drift analysis, retraining, release controls, and rollback.
• The ability to balance model quality with reliability, interpretability, security, privacy, latency, throughput, and cost.

⭐ Desirable Experience

• An opening may emphasize predictive models, computer vision, natural language models, ranking, anomaly detection, recommender systems, edge inference, generative AI model operations, or an enterprise ML platform.
• Specific openings may name Python, SQL, Java, model frameworks, distributed-processing tools, cloud ML services, container orchestration, graphics processing units, feature stores, model registries, experiment tracking, or infrastructure as code.

🎁 Benefits

Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening.

🛂 Visa & Eligibility

Not specified