Top Machine Learning Jobs in Washington, DC

Develops algorithms, predictive models, quantitative analyses, and visualizations from structured and unstructured data. Collaborates with clients to understand analytical needs, explores and cleans data, applies machine learning, AI, NLP, and text-mining techniques, and translates complex datasets into actionable insights for informed decision-making across public and private sector challenges.

Designs and evaluates machine learning solutions for government mission data. Responsibilities include assessing dataset quality and readiness, developing models and reproducible workflows, defining evaluation metrics, conducting error analysis, addressing bias and drift, and using AWS SageMaker for experimentation, pipelines, training, model registration, and deployment. Provides architecture and implementation recommendations while supporting batch or real-time processing decisions in secure operational environments.

Build and validate analytical models using large structured datasets to improve operational and business outcomes. Translate complex findings into actionable recommendations, partner with engineering, design, and product teams to deploy production-ready solutions, lead high-impact federal projects, mentor junior staff, and shape data science strategy for Department of Education programs.

Leads end-to-end advanced analytics projects supporting regulatory operations and cross-market surveillance. Defines quantitative approaches, develops predictive pipelines, drives data and infrastructure availability, and delivers analytics capabilities across teams. Engages stakeholders, gathers use cases, provides research and insights, mentors analysts, and advances enterprise analytics strategy. Requires strong programming, machine learning, data mining, and analytical skills in a hybrid environment with occasional travel.

Develop data science algorithms, analytic software, ETL pipelines, and predictive models using structured and unstructured data. Partner with clients to understand analytical needs, create data visualizations, integrate software solutions, and guide teammates. The role requires experience with Python or R, statistical and machine learning methods, version control, and data engineering workflows. Work may support intelligence and operations analysis and requires TS/SCI clearance.

Lead complex data exploration and analytics for DoD/IC, develop and apply statistical and AI/ML models, evaluate AI methodologies, perform TEVV and AI risk mitigation, and guide teams working on classified networks to produce actionable intelligence.

Lead advanced data exploration and analytics for national security clients: evaluate AI and LLM methodologies, develop TEVV and ML validation in CI/CD, visualize risks for leadership, guide teams, and work on classified networks to support DoD/IC missions.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Collaborate with clients to understand data needs, lead solution development, apply machine learning and NLP techniques, and translate complex information into actionable insights. The role also involves guiding teammates and advising clients across public and private sector challenges.

Leads strategic research and quantitative surveillance initiatives involving market manipulation, regulatory investigations, large distributed datasets, statistical analysis, and machine learning. Designs scalable analytical solutions, presents findings to senior personnel, supports regulatory impact assessments, recommends strategic data procurement, and contributes to surveillance program evolution and special projects.

Lead and grow Elliptic’s data science team while remaining technically hands-on. Own research direction, datasets, machine learning models, governance, hiring, performance management, and cross-functional delivery. Build capabilities for compliance involving autonomous agents, improve reliability and throughput, and guide model validation, monitoring, and remediation. The role requires strong Python and SQL skills, modern cloud data-stack experience, AI fluency, technical leadership, and expertise analyzing large behavioral or transactional datasets.

Leads technical foresight on how AI and emerging technologies may reshape global finance. Assesses AI capabilities, architectures, scaling, constraints, governance, failure modes, and systemic risks; translates findings into scenarios, hypotheses, briefings, research priorities, and thought leadership. Partners with research, standards, advocacy, and industry stakeholders to stress-test assumptions and evaluate technology’s implications for investment workflows, institutions, markets, professional roles, and standards.

Analyze federal workforce data and position descriptions using statistical modeling, NLP, machine learning, and text analytics. Develop dashboards, visualizations, and reports; map duties and skills to job series and grade levels; validate findings with HR professionals and leadership; and provide recommendations supporting accurate, compliant job classification and reclassification decisions.

Design, create, and implement AI and ML systems for Defense and Intelligence sectors, ensuring scalability and integration into various environments.

Build, deploy, and maintain production machine learning models and end-to-end ML pipelines. Develop MLOps frameworks with model versioning, CI/CD, registries, feature stores, monitoring, and automated retraining. Deploy solutions using cloud and containerized technologies, troubleshoot performance issues, address model drift, and collaborate with engineering and data teams on scalable mission-critical applications.

Leads architecture and implementation of enterprise AI and data platforms supporting vector search, RAG, LLM, and Agentic AI applications. Establishes data governance, lineage, traceability, monitoring, and evaluation controls; drives technical decisions around scalability, security, reliability, and cost. Partners with engineering, data science, cybersecurity, and mission teams, communicates technical tradeoffs to executives, evaluates emerging technologies, and mentors engineers. The role requires active TS/SCI with CI polygraph clearance and extensive cloud, data engineering, MLOps, and AI platform experience.

Analyze data to identify trends and anomalies, develop machine learning and statistical models, perform data parsing and ETL with Python and SQL, create dashboards, and collaborate with data engineers. The role emphasizes NLP, LLM, and Generative AI applications, quantitative analysis, model validation, and experimentation with emerging tools for federal clients.

Analyzes complex datasets using statistical methods, machine learning, and AI techniques. Builds and evaluates predictive models, develops reproducible analytical workflows, supports production deployment, and applies NLP, generative AI, LLMs, deep learning, and time-series methods. Partners with engineers and stakeholders to deliver mission-focused solutions, documents findings, and supports responsible AI practices including explainability, trustworthiness, and risk management.

Build and maintain large-scale machine learning infrastructure, including training-data pipelines, distributed model-training workflows, and orchestrated ML pipelines. Improve dataset validation, monitoring, testing, reproducibility, and system performance while partnering with ML engineers and researchers to translate research ideas into reliable production systems.

Lead the technical direction of GenAI products and knowledge systems. Design and scale infrastructure for training, fine-tuning, serving language models and embeddings, and build retrieval, hybrid search, and RAG pipelines. Partner with applied scientists and engineering teams to rapidly prototype, productionize, and optimize reliable ML services. Establish best practices for deployment, monitoring, evaluation, latency, throughput, and resource efficiency.

Design, develop, and operationalize AI and machine learning solutions for production applications and data workflows. Build data and model pipelines, implement MLOps and LLMOps practices, support generative AI and RAG systems, and contribute to CI/CD, observability, responsible AI, governance, and model lifecycle management. Collaborate with data science, software engineering, and cloud teams in secure federal environments.

Leads complex data exploration, analytics, statistical modeling, and AI evaluation projects supporting strategic intelligence. Develops scalable testing libraries, validates AI and machine learning systems, performs TEVV and risk mitigation, and communicates complex findings and system risks to nontechnical leadership. The role requires advanced analytics experience with LLMs or agentic AI, CI/CD validation, classified DoD or Intelligence Community networks, and a TS/SCI clearance with current or attainable CI Polygraph.

Reviews and evaluates advanced analytics for DIA finished intelligence. Applies mathematics, statistics, data science, artificial intelligence, and machine learning to exploratory analysis, predictive modeling, wargaming, strategy development, and complex data interpretation. Advises on data management under infrastructure constraints, produces technical narrative products, and supports classified DoD and intelligence community missions. May also review technical documentation, develop training, and support multi-INT analytics.

Develop and execute go-to-market strategies for Physical AI and robotics technologies. Identify customers, assess their needs, promote technical capabilities, drive sales opportunities, and establish strategic partnerships. Apply software engineering and AI expertise to evaluate capabilities, identify gaps, and translate technical developments into products and customer solutions. Collaborate with data scientists, engineers, researchers, vendors, and external organizations to commercialize AI-enabled offerings and expand business growth.

Develop, train, test, deploy, and maintain machine learning models supporting defense and national security applications. Apply statistical analysis, deep learning, computer vision, generative AI, and data-handling techniques to large datasets. Collaborate with data engineers, data scientists, architects, and product owners, guide clients on ML tools and frameworks, solve technical problems, and help define AI strategy.

Develop, train, test, deploy, and maintain machine learning models for national security and Department of Defense applications. Collaborate with data engineers, data scientists, solution architects, and product owners to deliver AI solutions, guide clients on ML algorithms and frameworks, and solve complex business and mission problems. The role requires Python and ML framework expertise, experience with deep learning, computer vision, or generative AI, a bachelor's degree, and Secret clearance.