Top Machine Learning Jobs in Washington, DC

The Expansion & Vertical Marketing Manager drives global marketing strategies, focusing on product-led expansion, vertical execution, and scalable programs rooted in customer outcomes and aligned with business objectives.

As a Machine Learning Engineer, you'll design, build, and maintain models to detect security threats, ensuring model efficiency and aligning initiatives with business goals.

Lead the vision and strategy for the Commerce Intelligence Platform, transforming data assets into scalable, revenue-generating products, fostering cross-functional collaboration and driving market growth.

The VP of Client Development will lead InMobi's growth strategy within the OEM and Telco sectors, establishing executive partnerships, joint business plans, and aligning product innovation to maximize market opportunities. The role requires extensive experience in enterprise partnerships and strategic business development with global technology leaders.

Lead Yahoo's Data Activation product area: define vision and strategy, partner with Data Science and Engineering, drive cross-vertical data activation initiatives, deliver measurable business impact, and mentor/manage product teams to execute on audience, personalization, and targeting use cases.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead a team to enhance Cengage Learning Platforms by implementing AI-first strategies, improving software delivery processes, and ensuring product quality.

Lead the Performance ML team at MNTN, driving strategy and deployment of predictive models for ad technology, while managing, mentoring, and scaling engineering teams.

Join AIS supporting a federal client in the DMV to develop, train, and deploy AI/ML models from ideation to production. Use Python and common data-science libraries, work with database systems, collaborate on hybrid teams, and meet security clearance requirements.

Work with clients to formulate questions, explore and clean structured and unstructured data, build predictive models and algorithms, develop automated analytic software and ETL pipelines, and create visualizations and insights to support decision-making in intelligence and commercial contexts.

Lead Billtrust's Applied Intelligence product strategy, build and scale AI product teams, define governance and evaluation standards, translate frontier AI into production-grade AR solutions, and represent the company externally to drive commercial impact and customer co-creation.

Senior product leader setting the multi-year Applied Intelligence vision and roadmap for AI-powered B2B AR products. Own end-to-end AI product lifecycle, governance, evaluation standards, talent building, cross-functional partnership, and external representation to drive commercial impact and customer adoption.

Lead operationalization of AI strategy by implementing scalable AI platforms, standards, and governance. Identify and prioritize high-impact AI and analytics use cases, drive end-to-end delivery into production, embed AI into business processes, manage AI teams, mentor practitioners, and evaluate emerging AI/GenAI technologies to ensure responsible, secure, and measurable business impact.

Develop and lead data science solutions: build ETL pipelines, clean and model structured/unstructured data, design algorithms and automated analytics, produce visualizations, use ML methods, collaborate with clients and mentor teammates to turn data into actionable insights for government and commercial problems.

Lead and coach Agile software teams, facilitate Scrum ceremonies, manage timelines, risks, and stakeholder communication, and support delivery of AI-enabled solutions across client engagements in a distributed consulting environment.

The Advanced Technologies Consultant at Epiq advises clients on AI and analytics in document review, managing multiple projects and workflows, while serving as a subject matter expert.

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.

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 fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, pricing models, vintage and NPV analyses, executive reporting, model integration and testing, and explore alternative data to optimize approval rates and portfolio health.

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Lead quantitative credit analytics for a Send Now Pay Later (SNPL) product: build and maintain credit policies, pricing and NPV models, vintage analysis, portfolio monitoring, model integration/testing, and alternative data evaluation to optimize approval rates and manage portfolio health.