Top Machine Learning Jobs in Washington, DC

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.

Work with clients to translate data into actionable insights using machine learning, AI, and IoT data. Lead development of algorithms, automated analytic software, ETL pipelines, and data visualizations; integrate FOSS/COTS and support model deployment. Must hold TS/SCI clearance.

Lead the Accounting & Finance Reporting, Data & AI Center of Excellence: set strategy, governance, roadmap, and deliver analytics, automation, and AI solutions. Own intake, prioritization, implementation, adoption, and value realization across financial processes while partnering with IT and mentoring the team.

Designs, implements, and maintains automation workflows and IaC/RPA solutions; applies AIOps and ML-driven analytics to detect, correlate, and resolve infrastructure issues; optimizes capacity, performance, patching, backups, and reporting; collaborates with cross-functional teams.

The Director will lead and manage the firm's AI and data science initiatives, oversee a multidisciplinary team, and develop AI solutions that enhance operational efficiency and decision-making in legal contexts.

Lead strategy and delivery for an AI-powered claims decision-support and auto-adjudication platform. Define vision and roadmap, manage a team of product managers, partner with business, engineering, and data science, and drive adoption, impact measurement, and executive-level communication across a complex, matrixed enterprise.

Apply data exploration, cleaning, analysis, visualization, and predictive modeling to structured and unstructured data. Develop algorithms and ML/NLP solutions, work with clients to answer questions, and use distributed data tools to produce actionable insights.

Lead data science engagements: work with clients to define problems, build ETL pipelines, develop machine learning models and algorithms, create visualizations, guide teammates, and deploy analytic solutions to inform decision-making across classified and unclassified environments.

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.

Develop and automate Python-based ETL and data pipelines in Databricks; clean and preprocess manpower/personnel data; build, train, and deploy machine learning models; create Qlik/Tableau reports and dashboards; maintain historical data and troubleshoot ETL; produce documentation and data products; collaborate with cross-functional teams to translate requirements into data-driven solutions.

Lead complex analytics projects for DoD/intelligence clients using analytic methodologies, knowledge graphs, ontologies, and semantic web standards. Design data models, enable semantic retrieval and reasoning, ensure interoperability across data sources, guide teams, and provide quantitative and qualitative analysis to senior leaders.

Lead document-vision research and production systems for multimodal legal evidence understanding. Define evaluation standards, frame hard problems, design and ship models, run efficacy studies and monitoring, mentor senior scientists, and advise leadership on scientific direction.

Lead and grow an applied science team building reliable, production-ready generative AI systems for legal use. Own end-to-end AI system readiness, set technical direction, perform evaluations and error analysis, partner cross-functionally to move work to scale, hire and coach staff, and represent the team externally.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Lead product vision and roadmap for the Hivemind AI Platform to enable training, evaluation, governance, and safe edge deployment of foundation and domain models. Drive MLOps, data/simulation pipelines, synthetic data generation, model governance, and integration with edge hardware. Partner cross-functionally to productionize research, ensure accreditation and safety, and support continuous learning and sovereign AI factory capabilities for mission-critical autonomy.

Use data exploration, cleaning, modeling, and visualization to turn structured and unstructured IoT and other data into actionable insights. Develop predictive models, algorithms, and visualizations, work directly with clients, and apply ML/AI/NLP techniques to solve problems across public and private sectors.

Perform data exploration, cleaning, analysis, and visualization; develop predictive models and algorithms on structured and unstructured data; apply ML/AI/NLP techniques to deliver client-facing solutions. TS/SCI clearance required.

Use data exploration, cleaning, modeling, and visualization to turn structured and unstructured data into actionable insights. Develop predictive models and algorithms, apply ML/AI/NLP and text-mining techniques, and collaborate with clients to solve real-world problems.

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.

Work within the CEO's office to design and implement AI-powered workflows, transform unstructured information into actionable materials, prepare executive briefings, support cross-functional teams, build internal knowledge systems, and continuously identify and deploy AI solutions to eliminate repetitive work and increase productivity.

Pre-sales technical lead for federal accounts who builds and delivers AI/ML and GenAI demos, designs compliant solutions (FedRAMP/DoD IL/NIST/FISMA), supports pilots/proofs, advises on data readiness and security, and partners with sales to drive federal adoption.

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.