Top Senior Machine Learning Jobs in Washington, DC

Own end-to-end product direction for complex customer data and AI engagements, from executive discovery and value definition through MVP design, PRD creation, technical delivery, production launch, adoption, and strategic handoff. Partner with sales, engineers, and senior stakeholders to shape architectures, prioritize backlogs, manage risks, ensure production readiness, and deliver measurable business outcomes.

Lead end-to-end product design for an AI-powered women’s health platform. Responsibilities include defining UX vision, creating interaction models, information architecture, wireframes, high-fidelity interfaces, prototypes, design systems, and user research. Collaborate with product, engineering, ML, marketing, and leadership teams to translate business needs into customer-focused solutions, manage design initiatives, and measure product success through user feedback and metrics.

Serves as the primary technical contact across sales cycles and implementations for identity verification products. Responsibilities include presenting solutions, designing proof-of-concepts, developing SOWs and pricing, translating technical concepts, coordinating stakeholders, advising clients, gathering product feedback, supporting sales engagements, contributing to thought leadership, and mentoring junior consultants. The role requires enterprise software experience, SaaS sales knowledge, strong technical aptitude, statistical communication skills, and up to 30% travel.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Own the strategy, roadmap, and investment priorities for foundational agricultural data products, including pipelines, data models, APIs, and shared services. Partner with engineering, architecture, data science, and product teams to deliver scalable, governed, high-quality data capabilities. Prioritize initiatives based on customer and commercial value, define success metrics, resolve cross-team tradeoffs, and expand data support for analytics, predictive modeling, machine learning, and AI applications.

Lead UX design initiatives across a healthcare product suite, solving complex user and business problems through research, prototyping, interaction and visual design. Mentor designers, influence cross-functional teams, contribute to the design system, and establish scalable design standards. Collaborate with engineering, product, leadership, and data science teams to integrate ML features and deliver intuitive, consumer-quality healthcare experiences.

Lead UX design initiatives across a healthcare product suite, solving complex problems through prototyping, user research, and strategic design. Collaborate with engineering, product, leadership, and data science teams to create consumer-quality experiences, integrate ML features, and evolve scalable design systems. Mentor designers, resolve stakeholder feedback, establish design standards, and drive innovation across large-scale projects.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Analyze product, user, transactional, and repayment data to develop risk and underwriting models, reduce losses and fraud, monitor model performance, and guide product and growth strategy. Build Looker dashboards and LookML models, conduct segmentation and experimentation, measure CAC and LTV, and communicate actionable insights to product, engineering, marketing, finance, and leadership.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Own the production lifecycle of ranking and recommendation models, including training, validation, deployment, monitoring, retraining, versioning, rollback, and retirement. Build scalable Spark-based feature and data pipelines, orchestration workflows, model hubs, observability, CI/CD, and reliability standards. Partner with data science and engineering teams to improve model quality, deployment speed, infrastructure efficiency, and operational resilience while mentoring teammates.

Build and scale accounting software synchronization solutions connecting BILL’s payment network with third-party accounting platforms. Lead end-to-end software design and delivery, develop scalable AWS microservices, improve platform performance, guide testing and CI/CD practices, resolve customer problems, mentor engineers, and use generative AI to accelerate development.

Lead the design, development, deployment, and operation of production-grade generative AI and LLM solutions. Build data and ML pipelines, fine-tune and evaluate models, implement RAG and prompt engineering, and establish MLOps practices on AWS. Drive use cases across insurance workflows, ensure security and responsible AI compliance, mentor engineers, and collaborate with product, engineering, security, finance, and go-to-market teams.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Leads data scientists and machine learning engineers developing personalized customer experiences. Sets strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; oversees machine learning systems from design through deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability practices; and manages capacity, resources, technical standards, and team development.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

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.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

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 deploy machine learning models for fraud detection and credit risk decisioning. Own the full ML lifecycle, including data exploration, feature engineering, experimentation, validation, deployment, monitoring, and optimization. Analyze fraud trends and portfolio outcomes, collaborate with Product, Engineering, and Risk Operations, apply generative AI tools, and mentor fellow scientists while delivering measurable improvements in fraud prevention and responsible credit access.

Own the complete machine learning model lifecycle, from experimentation and validation through deployment, monitoring, retraining, rollback, and retirement. Build reusable training, evaluation, deployment, orchestration, and CI/CD infrastructure. Establish model promotion standards, versioning, lineage, monitoring, alerting, and incident-response processes. Partner with data scientists to preserve scientific validity, improve reliability and efficiency, support delayed-label use cases, mentor teammates, and define MLOps priorities.