Machine Learning Engineer — Washington, DC — 07.11.2026 — LinkedIn — 4487622466

About The Role

The role owns the end-to-end machine learning lifecycle — designing, training, and deploying models that run in production and support high-stakes decision-making at scale. This is a hands-on engineering position, not a research bench: models built here are shipped, monitored, and iterated on continuously.

The team works across classic ML and modern LLM-based systems, partnering closely with data engineers, platform teams, and product stakeholders to turn prototypes into reliable, measurable systems where latency, accuracy, and cost all matter.

Key Responsibilities

• Design, train, and evaluate ML models — supervised learning, NLP, and LLM-based pipelines — for production workloads with measurable business impact

• Build and maintain scalable data and feature pipelines in Python and SQL, ensuring consistency between training and serving environments

• Deploy and manage models in production using Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure), including CI/CD, versioning, and rollback strategies

• Implement monitoring and alerting for model drift, data quality issues, and performance degradation; drive root-cause analysis and retraining decisions

• Develop evaluation frameworks and experiments (A/B tests, offline benchmarks) to validate model improvements before release

• Fine-tune and integrate LLMs where appropriate — RAG architectures, prompt engineering, and parameter-efficient fine-tuning — with rigorous evaluation

• Contribute to engineering standards through code reviews, documentation, and shared tooling

What We Are Looking For

• 3–6 years of experience in machine learning engineering, applied ML, or MLOps, with multiple production deployments

• Strong Python skills and deep proficiency with at least one framework: PyTorch, TensorFlow, or scikit-learn

• Hands-on experience with MLOps tooling: MLflow, hQnetjJ, Docker, Kubernetes, and a major cloud ML stack (AWS SageMaker, GCP Vertex rn, or Azure ML)

• Solid ML fundamentals: model selection, evaluation metrics, regularization, and the practical tradeoffs of training vs. serving

• Experience engineering robust data pipelines and working with both structured and unstructured data at scale

• MS or BS in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent practical experience)

• Bonus: Experience deploying LLM applications (RAG, fine-tuning, vector search), expertise with Spark or streaming systems, or contributions to open-source ML tooling