Top Senior Machine Learning Jobs in Washington, DC

Own fraud strategy and prevention for a consumer lending product, including fraud typologies, repayment and chargeback analytics, synthetic identity and account takeover detection, decision-engine rules, machine learning risk-model deployment, blocking protocols, investigation workflows, fraud metrics, and regulatory coordination for SARs and loan abuse.

Own the design, development, integration, release, and reliability of tailored AI solutions for enterprise clients. Adapt a core AI platform, deliver client-specific changes to a shared codebase, manage production rollouts, diagnose complex issues, and serve as the primary technical contact for client engineers. The role requires strong Python, software architecture, system design, and experience deploying LLM or agent-based systems used by customers in live environments.

Leads enterprise Data and AI strategy, use-case prioritization, governance, risk assessment, solution delivery, performance monitoring, and adoption. Partners with Finance, IT, Legal, Compliance, Privacy, Security, and business stakeholders to develop responsible AI and analytics solutions. Provides technical guidance on machine learning, generative AI, data requirements, prompt design, model selection, testing, and implementation. Creates training, communications, and reusable assets to improve AI literacy and adoption.

Lead complex data exploration, analytics, and AI evaluation projects supporting Department of Defense and intelligence missions. Develop scalable testing libraries, assess advanced analytics and AI system performance, and create TEVV and risk-mitigation procedures. Translate complex technical findings into clear visual and narrative products for leadership, highlighting system risks and go/no-go criteria. Work with classified networks, guide mission teams, and evaluate AI robustness, fairness, and compliance.

Lead product strategy, roadmap, and delivery for Falcon Complete’s internal MDR tooling, including AI-powered investigation and triage, ticketing, case management, and analyst workbench systems. Partner with security operations, analysts, engineering, and data science teams to translate workflows into product requirements, prioritize automation, improve analyst productivity and response times, define success metrics, and enhance customer outcomes.

Own strategic partner relationships from onboarding through ongoing delivery, serving as the primary point of contact and internal advocate. Lead cross-functional implementations, track commitments, translate partner needs into product and engineering requirements, support roadmap planning, manage escalations, monitor account health, and identify growth opportunities. Establish partner cadences, scalable processes, and centralized request tracking while ensuring partners are prepared to adopt new features.

Own strategic partner relationships, onboarding, implementation, account health, escalations, and recurring partner cadences. Translate partner needs into product and engineering requirements, influence roadmap priorities, and ensure commitments are delivered. Build a scalable partner management function, including request tracking, operating cadences, onboarding processes, and implementation playbooks. Collaborate across Sales, Product, Engineering, Data Science, and Commercial teams to support partner retention, growth, and successful feature adoption.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Leads AI and computational algorithm research, development, clinical validation, deployment, and lifecycle monitoring for oncology diagnostics. Sets foundation model and multimodal AI strategy, prioritizes clinical use cases with Product and clinical stakeholders, manages multidisciplinary algorithm teams, recruits technical leaders, and ensures regulatory-quality data, validation, reproducibility, release controls, and post-launch performance monitoring.

Own and build Fireworks’ customer marketing function, including advocacy, enterprise reference programs, customer stories, peer-to-peer events, co-marketing, awards, and customer-led expansion initiatives. Recruit and develop technical and executive advocates, create compelling proof for sales and marketing, manage customer approvals, and collaborate with Sales, Customer Success, Product Marketing, Developer Relations, Product, and Events. The role requires hands-on strategy and execution in a fast-moving AI infrastructure company.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Lead the technical aspects of enterprise security sales, including discovery, tailored demonstrations, proof-of-value engagements, RFP responses, and architecture discussions. Build relationships with SOC analysts, security architects, and CISOs; translate customer feedback into product improvements; create reusable sales enablement assets; and support early implementation handoffs. The role requires security operations expertise, cloud knowledge, scripting ability, and familiarity with AI and machine learning concepts.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Build product enablement for global go-to-market teams across AI Agents, APIs, and Creative products. Responsibilities include preparing launch workshops, competitive training, product certifications, onboarding and everboarding content, hands-on demos, role-play simulations, practice environments, and performance measurement. The role partners with product marketing, engineering, research, and enablement teams while using ElevenLabs’ AI technology to create training experiences.

Analyzes large-scale telemetry and datasets to identify content delivery performance and reliability issues. Builds software tools to automate analytical workflows and enable autonomous diagnosis. Applies statistical analysis, machine learning, networking, and distributed-systems expertise to develop targeted platform improvements. Defines and mines internal data feeds, establishes performance indicators, and partners with Engineering, Operations, and Product teams to guide feature and process adoption.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.

Own the end-to-end mobile identity verification product across SDKs, web flows, and APIs. Lead roadmap, UX, biometric intelligence, deepfake detection, liveness technology, conversion optimization, fraud prevention, and global scalability. Partner with engineering, research, design, customer success, and commercial teams to deliver scalable offerings, support customer adoption, identify upsell opportunities, and use analytics and experimentation to improve performance.

Build and deploy production machine-learning models for member identification, personalization, provider search, and healthcare AI tools. Lead projects from problem framing and prototyping through product handoff, design experimentation and causal analysis, define product metrics, and develop reliable feature pipelines and experiment infrastructure. Partner with product, engineering, care operations, marketing, and analytics engineering while communicating recommendations clearly and ensuring models are monitored, tested, and impactful.

Senior Product Manager responsible for the vision, strategy, and roadmap of Root’s pricing platforms and rating engine. The role leads cross-functional prioritization across engineering, data science, analytics, actuarial, risk, legal, compliance, and state management. Responsibilities include accelerating model deployment, improving data services, building experimentation tools, governing APIs and event-driven architectures, reducing regulatory bottlenecks, and supporting expansion into new states and markets.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.

Leads the architecture, development, deployment, and production support of secure, scalable SaaS software across a public safety platform. Champions AI-native development practices, including coding agents, automated testing, code review, and agent workflows. Mentors engineers, collaborates cross-functionally, improves reliability and performance, manages incident response, and implements secure development practices for sensitive criminal justice data.

Develop and advance the organization’s Insider Threat Program by assessing capabilities, creating behavioral indicators, enhancing detection analytics, and investigating suspicious activity. The role analyzes security, fraud, and behavioral telemetry; configures monitoring technologies; reduces false positives; produces risk reporting and metrics; and partners with Cybersecurity, Fraud, HR, Legal, Risk, and Technology teams to improve insider-risk detection and response.

Lead research at the intersection of AI, cybersecurity, and computer systems. Define research problems, develop hypotheses, build prototypes, design rigorous experiments, evaluate AI and security methods, and translate findings into practical systems. Research areas may include AI agents, autonomous penetration testing, attack-path reasoning, cyber deception, AI safety, and security evaluation. Mentor junior researchers, collaborate with engineering and product teams, and communicate findings through reports, presentations, prototypes, or publications.

Independently lead complex technology and commercial transactions, including SaaS, cloud, licensing, vendor, partnership, and data-processing agreements. Advise cross-functional stakeholders on privacy, security, intellectual property, GDPR, AI, regulatory, and commercial matters. Improve contracting through templates, playbooks, CLM workflows, AI-enabled tools, analytics, and scalable legal operations. Analyze legislation and regulatory developments affecting cybersecurity, privacy, AI, and digital infrastructure.