Top Machine Learning Jobs in Washington, DC

Lead development of advanced analytics and AI/ML solutions (including LLMs/agentic AI) for classified clients. Guide teammates, translate complex data into visual and narrative deliverables, evaluate model performance, and advise leadership on risks and decision criteria.

Lead Audit’s AI, product, and data science strategy to build enterprise-grade AI/analytics/automation products and a unified audit platform. Drive platform architecture, model lifecycle, governance, and adoption while growing a high-performing team. Partner with Technology, Enterprise AI, risk, and business stakeholders to enable continuous monitoring, automated evidence generation, improved risk detection, and measurable efficiency and insight gains across the audit lifecycle.

Lead a forward‑deployed AI solutions team to translate enterprise AI capabilities into business outcomes. Partner with executives to co‑design and deliver priority use cases, drive AI productivity tool adoption, establish repeatable AI solution patterns, and feed learnings back into the AI platform and governance. Balance rapid delivery with enterprise rigor while coaching teams and managing stakeholders.

Lead and manage AI-focused consulting engagements: define hypotheses, oversee analytics and data collection, translate client needs into technical requirements, support ML/generative AI solution delivery from proof-of-concept to production, build executive relationships, and coach consultants to deliver measurable business impact.

Design and implement data science solutions: ingest and clean data, build ETL pipelines, develop predictive models and algorithms, create visualizations, and collaborate with clients to deliver actionable insights. Work often involves intelligence-domain data and requires active TS/SCI clearance.

Lead end-to-end data science work: exploratory and statistical analysis, data engineering, ML model development (recommendation engines, predictive systems), data visualization (Power BI, Tableau), and multi-int large-data analytics to support defense programs and produce actionable intelligence products.

Build and operate production machine learning systems focused on multilingual NLP, including entity linking, classification, event extraction, deduplication, and narrative analysis. Develop GPU-accelerated workflows, fine-tune transformer models, and create LLM features such as RAG pipelines and agents on AWS Bedrock. Establish evaluation frameworks, integrate ML services into Django and event-driven platforms, lead technical architecture discussions, and mentor team members.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.

The Machine Learning Engineer will develop and maintain ML deployment pipelines, operationalize AI models, and ensure system reliability and scalability.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

Own the full lifecycle of detection and tracking models, including data curation, annotation, training, evaluation, optimization, and deployment to embedded edge platforms. Develop object detection and multi-object tracking systems using mission-specific sensor data, diagnose performance issues, and collaborate with systems engineers to integrate models into the Altra platform. Optimize models for SWaP-constrained environments and support electro-optical and infrared modalities.

Lead design and delivery of production-grade ML and Generative AI platforms. Define technical vision, own full SDLC, architect scalable distributed ML systems, partner with cross-functional teams to integrate and operationalize models (RAG, agents, LLM fine-tuning) at scale.

Deploy and maintain state-of-the-art ML models (LLMs and vision) in production for government customers. Improve models via retraining, tuning, and architecture updates; build scalable ML infrastructure; prototype ML-driven product enhancements; work with massive datasets and multimodal fine-tuning pipelines; collaborate across research, product, and engineering. Requires active security clearance and light travel (~10%).

Summer data science internship working on ML projects (anomaly detection, numerical optimization, PII/PHI NLP). Interns will develop models, write high-quality Python, use Linux and development tools, work with git, Docker, CI/CD and Kubernetes, participate in code reviews and agile rituals, and present results to technical and non-technical audiences.

Develop and productionize AI features from PoC to scalable deployments. Write production-ready Python code, implement testing and debugging, refine user stories, participate in ethical and security reviews, and collaborate with stakeholders to align AI solutions with business needs.

Build and deploy credit risk models for acquisition and account management. Create datasets and features from traditional and non-traditional sources, collaborate with credit, product, and engineering partners, run experiments, and monitor models in production to enable responsible lending decisions.

Six-month on-site co-op building LLM-driven features and agents inside the Nuclear Operating System. Ship ML models and agent workflows, develop data pipelines (Foundry/AWS GovCloud), build predictive and anomaly-detection models on time-series sensor data, and own model quality, evals, and monitoring for production readiness.

Build and ship ML and LLM-driven features and agents inside the Nuclear Operating System (NOS). Develop data pipelines and integrations (e.g., Foundry, AWS GovCloud), create predictive and anomaly-detection models, and own model quality with evals, regression suites, and monitoring. Work on production systems alongside engineers and domain experts during a summer on-site internship.

Lead design and delivery of LLM- and agent-driven products end-to-end: build distributed agentic systems, retrieval and grounding pipelines, production inference stacks, evals and labeled-data flywheels, and partner cross-functionally to harden ML into reliable, mission-critical services.

The AI/Machine Learning Engineer will design, develop, and deploy AI models for healthcare, collaborating with teams to enhance patient outcomes using advanced data analytics and machine learning techniques.

Build and deploy agentic, full-stack AI systems using LLMs, embeddings, RAG, and agent orchestration for defense and intelligence customers. Collaborate with customers, operate in classified/air-gapped environments, codify reusable patterns, and support end-to-end delivery from prototype to production.

Design, develop, deploy, and maintain production ML models and MLOps pipelines. Build custom AI/ML algorithms, run experiments, evaluate and optimize model performance, and integrate models with cross-functional teams for mission-driven applications in a federal environment.

Design, develop, deploy, and maintain production ML models and pipelines. Build MLOps frameworks, run experiments, evaluate and fine-tune models, and integrate AI/ML solutions with cross-functional teams to deliver mission value for federal clients.