Top Senior Machine Learning Jobs in Washington, DC

Lead the development of deep learning models for email data, optimize models, design evaluation frameworks, integrate products, and mentor junior researchers.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Lead a consortium advancing AI-driven neurodegeneration research: set strategy, build partnerships, manage multidisciplinary executive team, oversee product development and deployment, secure funding, ensure ethical AI and scientific integrity, and scale the platform to broader biomedical domains.

Lead strategy and execution for MarketScan data products and delivery experiences. Define portfolio vision, prioritize data product innovation and operational excellence, drive cloud and AI enablement, ensure data quality/compliance, engage executives and customers, and build a high-performing product organization to grow adoption and revenue.

The Senior Software Engineer will design and optimize recommendation algorithms, maintain data pipelines, and improve personalized user experiences using machine learning techniques and backend engineering skills.

Lead enterprise platform sales by partnering with C- and VP-level executives to drive adoption of Aera's decision intelligence platform. Own pipeline, develop account strategies, close large strategic deals, expand usage within clients, and collaborate cross-functionally to meet ACV and revenue targets. Act as a trusted advisor and subject-matter expert in clients' industries.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

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.

Lead Safety Architect for autonomous vehicles, focusing on safety architecture, metrics, analysis, and collaborative processes for software delivery and safety standards.

The Senior Director of Information Technology leads the global IT function, aligning technology with business priorities, overseeing digital transformation, cybersecurity strategies, and IT infrastructure optimization.

As an Analytic Methodologist, you will lead analytics projects, develop and evaluate methodologies, and support the Department of Defense with your expertise in data science, knowledge graphs, and ontologies.

Support the Secretary's front office by modernizing Board processes through digital design, AI-enhanced analytics, and interactive visualizations. Deliver dashboards, multimedia content, and PowerPoint briefings; manage Board data, meeting logistics (Annual/Spring Meetings, IMFC), and front office workflows. Collaborate across teams to implement IT/AI solutions, automate reporting, and provide ad hoc HR, budget, and administrative support under tight deadlines.

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.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

Design, build, and scale backend systems and APIs for high-transaction consumer products. Collaborate with product, credit, compliance, support, and analytics teams. Contribute to code reviews, technical planning, security, and operational excellence. Mentor peers and promote engineering best practices.

Lead the development of generative AI tools for fashion, guiding ML engineers, and implementing innovative AI systems and workflows.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

The Senior Customer Success Manager drives revenue growth by ensuring customer satisfaction, building strong relationships, managing renewals, and promoting product solutions aligned with client goals.

Design and implement data-centric cybersecurity solutions: develop ML algorithms and automation for security tools; discover, parse, map, and transform legacy RMF artifacts into OSCAL; migrate and analyze security data; reproduce dashboards/workflows in eGRC; collaborate with Professional Services; produce migration assessments and reports.

Lead strategy and execution for AI-powered nutrition and coaching features. Define product roadmap, partner with engineering/data/design, run experiments and evaluations, ship 0-to-1 ML products, and ensure responsible AI practices and measurable user impact.

Lead product development for Socure's core fraud platform: build reusable services, APIs, and ML-powered features (graph, signals, model lifecycle). Drive prioritization, cross-functional delivery, KPI definition, customer discovery, and integration to improve detection accuracy and operational performance across fraud products.

Lead BI analytics and governance for client accounts: manage BI analysts, deliver dashboards and reports, oversee data mapping, modeling, quality, and platform initiatives (Power BI/Databricks), advise stakeholders, drive analytics roadmap, and apply advanced analytics and ML to optimize workplace and portfolio insights.

Lead and scale quantitative trading and liquidity strategies for NEAR Intents. Design game-theoretic models, build signal generation and execution systems, develop research and simulation infrastructure, translate research into production, monitor performance, and hire and mentor a quantitative research team while partnering with protocol and product teams.

Design, build, and operate production-grade AI systems using ML, LLMs, RAG, and agentic workflows. Define evaluation strategies, implement data/retrieval/orchestration layers, apply MLOps/LLMOps, and partner with stakeholders to prioritize, monitor, troubleshoot, and improve AI solutions balancing quality, latency, cost, reliability, and maintainability.