Top Machine Learning Jobs in Washington, DC

Design, build, and operate large-scale online ML inference infrastructure to serve production models with low latency and high reliability. Optimize inference performance, enable distributed training workflows, integrate ML pipelines with orchestration systems, improve observability, and support safe model rollout, canary testing, and automated rollback. Lead architectural improvements and collaborate with ML engineers and product teams to scale, monitor, and streamline model deployment and iteration.

Designs and implements AI solution architectures and strategies addressing client business needs and ethical considerations. Develops innovative solutions using AI, machine learning, deep learning, computer vision, NLP, and agentic AI. Leads technology assessments, evaluates alternatives, develops requirements, conducts independent research, translates complex technical topics for broad audiences, and mentors team members. Requires Python programming, modern software design knowledge, a STEM degree, and Secret clearance eligibility.

Designs, develops, and operationalizes machine learning models and advanced analytics for a 24x7 Security Operations Center. Analyzes security telemetry, builds data pipelines and feature engineering workflows, integrates analytics with SOC tools using Docker and Kubernetes, and applies threat frameworks such as MITRE ATT&CK. Provides technical leadership, mentors analytics personnel, collaborates with SOC analysts and engineers, and delivers documentation, performance metrics, and leadership briefings.

Lead forensic technology engagements by designing and deploying machine learning models, statistical algorithms, and data pipelines for digital investigations and electronic discovery. Collaborate with clients to define goals, analyze cloud and enterprise data, develop reusable automation components, communicate findings to executive audiences, and deliver training. The role also involves managing people on high-profile consulting engagements and providing client-facing technical leadership.

Builds dashboards, applications, automations, ETL integrations, predictive risk models, and AI/ML solutions for unstructured data. Performs data normalization, visualization, business intelligence analysis, and risk assessment improvement. Recommends data storage and integration solutions, creates Power BI documentation, and applies SQL, R, Python, and other data science tools to federal systems and public-sector projects.

Lead GenAI strategy and implementation for government customers, serving as a technical advisor and hands-on builder. Identify high-value use cases, design architectures using the OpenAI API, develop prototypes, guide production deployments, manage customer and partner relationships, and provide feedback to product and research teams. The role also involves scaling deployment engineering practices, publishing technical resources, and collaborating across sales, engineering, research, and policy teams.

The Data Scientist will support Army network and enterprise information environment missions by analyzing complex datasets, developing statistical and machine learning models, integrating APIs and automated data pipelines, and building Palantir Foundry and Army Vantage applications. The role includes creating executive visualizations, applying AIP tools, supporting secure Army and DoD data environments, and translating technical findings into actionable recommendations for senior leadership.

Analyze complex structured and unstructured datasets to answer client questions, evaluate AI and machine learning models, and deliver objective insights for leadership decisions. Collaborate with clients and teammates, mentor colleagues, and strengthen data science capabilities supporting Department of War or Intelligence Community clients. The role may involve GEOINT, LLMs, and all-source analysis in a classified environment.

Conducts data analytics, engineering, mining, statistical analysis, predictive modeling, and intelligence-focused data visualization. Designs and optimizes data architectures, storage, access, integration, throughput, and query performance. Builds AI automation tools and prediction systems from disparate data sources, collaborates with intelligence community members, communicates complex findings, and develops concise defense intelligence products. Requires active TS/SCI clearance, U.S. citizenship, and ability to obtain a counterintelligence polygraph.

Develop and deploy machine learning models and statistical algorithms to detect financial crime using large-scale BSA/AML transaction data. Perform exploratory analysis, feature engineering, validation, SQL analysis, visualization, and documentation using Python, R, PySpark, AWS, and related tools. Collaborate with investigators, compliance analysts, and technical stakeholders to translate regulatory requirements into analytical solutions. The role requires active Top Secret clearance with SCI eligibility and full-time onsite work in Washington, DC.

Develop and deploy NLP and machine learning models for classification, sentiment analysis, entity extraction, information retrieval, and user intent detection across large-scale legal, financial, news, and business datasets. Design scalable data science pipelines, evaluate modern AI algorithms and tools, translate business needs into technical solutions, and collaborate with product and engineering teams. Mentor junior scientists and contribute to model deployment, monitoring, maintenance, and development best practices.

Lead data science for defense clients: perform data engineering, mining, exploratory/predictive/statistical analysis; build ML/AI tools (recommendation engines, automated scoring); manage large, disparate data sources using R/Python/SQL; create visualizations in Power BI/Tableau; support Multi-INT analytics and provide senior-level analytic products for the defense cover program.

The Software Engineer, Applied AI will develop machine learning solutions and classification models, enhancing text, image, and video understanding on a policy intelligence platform.

Design and lead end-to-end AI solution architectures, ensuring scalability, security, and compliance. Translate business and regulatory requirements into technical designs, evaluate tools/platforms, document architecture standards, coordinate technical teams, and support deployment and ongoing improvements.

Lead the architecture and production deployment of agentic AI systems for classified and mission-critical government environments. Build systems for geospatial reasoning, retrieval, embeddings, persistent memory, orchestration, evaluation, and reliability. Own shared ML infrastructure, define technical direction, partner across engineering and research teams, mentor engineers, and drive ambiguous initiatives from prototype to supported production capability.

Work with clients to analyze large, structured and unstructured data sets, design and test AI/ML models, mentor teammates, and deliver actionable insights to leadership, primarily supporting DoW/IC customers.

Owns the architecture, development, evaluation, and production deployment of generative AI, agentic AI, computer vision, and reinforcement learning systems for mission-critical government applications. Responsibilities include building scalable ML infrastructure, improving models through tuning and retraining, developing evaluation benchmarks, partnering with users and research teams, mentoring engineers, and designing for classified and resource-constrained environments.

Architects enterprise AI/ML systems, including training pipelines, inference services, feature stores, data ingestion, and MLOps frameworks. Leads multidisciplinary engineering teams, defines technical roadmaps, oversees execution, and interfaces with senior government stakeholders. Establishes secure engineering, DevSecOps, testing, and model governance practices while guiding cloud-native integrations, advanced ML tool adoption, responsible AI, architecture reviews, and strategic planning for Defense Intelligence Agency missions.

The Senior Analyst will develop AI and ML algorithms, optimize data systems, and present research findings. Requires extensive experience in model development, system architecture, and MLOps.

Design, develop, deploy, and iterate production machine learning and generative AI solutions. Responsibilities include model development, NLP, prediction, anomaly detection, LLM and RAG integration, data pipelines, feature engineering, evaluation, monitoring, API integration, and architecture decisions. The role collaborates cross-functionally to balance accuracy, latency, cost, scalability, and maintainability.

Work with clients to analyze large structured and unstructured data sets, develop and evaluate AI/ML models, mentor teammates, and deliver actionable insights to leadership. Support DoW/IC customers and translate complex data into decision-ready products.

Design and lead large-scale machine learning and backend systems for advertiser growth, including budget pacing algorithms, GenAI creative generation infrastructure, marketplace experimentation, high-throughput billing pipelines, and real-time stream processing. Analyze marketplace and financial data, optimize advertiser performance, ensure billing accuracy and fault tolerance, lead multi-quarter technical roadmaps, and mentor senior engineers.

Lead federal engagements to design compliant AI/NLP and RAG knowledge-management solutions. Develop integrations, influence product strategy, build relationships with agency leaders, support sales, and produce federal-focused technical materials.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

Lead the development and implementation of AI initiatives across the Department of Defense, ensuring ethical, safe, and effective AI adoption while maintaining compliance with principles of responsible AI.