Machine Learning Engineer Role

About OPEN Data Jobs

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.

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.

Today, you can apply to any Opening that fits you through our secure application system. You can also register for a Role to express interest in that broader category of work. A Role registration is not an application to every future Opening.

The work

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.

The role centers on the model lifecycle and the platform beneath it. Machine Learning Engineers automate training and validation, manage features and model artifacts, optimize inference, implement machine learning operations (MLOps), and watch for changes in data, behavior, performance, reliability, and cost. They create the shared tooling that lets data scientists and application engineers move models into production safely.

What you'll build

· 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.

Who you are

You are comfortable at the seam between modeling and software engineering. You can inspect a model, harden a pipeline, diagnose a production failure, and improve the platform so the same class of problem is easier to prevent next time.

You value repeatability over heroics. You work closely with data scientists on model behavior, data engineers on reliable inputs, AI Engineers on application integration, and platform and security teams on the environment in which the model runs.

What you bring

• · 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.
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Requirements

What openings may require

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. Some openings will focus more on model development, while others will focus more on serving and platform engineering.

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. OPEN Data Jobs will identify the required depth for each opening