Top Senior Machine Learning Jobs in Washington, DC

Lead enterprise AI security architecture, strategy, governance, and secure-by-design implementation across cloud, hybrid infrastructure, software, and AI/ML environments. Design security patterns for LLM hosting, agentic systems, vector databases, and inference endpoints; translate regulatory requirements into technical standards; review complex architectures; implement AI security guardrails and policy enforcement; advise engineering and product teams; and mentor teams while influencing senior stakeholders.

Leads data and analytics transformation strategy for major client programs, defining roadmaps, use cases, architecture direction, and operating models. Advises senior stakeholders, facilitates strategy workshops, aligns programs to KPIs and value frameworks, supports governance and risk management, contributes to proposals and pre-sales, and mentors consulting and analytics teams.

Own Secret Detection and Vulnerability Research as a product: define strategy across prevention, detection, validation, revocation, and reporting; ship detection content and intelligence with measurable quality; drive adoption, retention, and revenue; partner with engineering, research, and Field; use AI to accelerate research and productization.

Source, develop, and close enterprise health system clients across a US territory. Build relationships with healthcare executives, launch and expand pilot programs, create account plans, meet revenue targets, and transition customers to Client Success. Collaborate with product, legal, marketing, support, and leadership; provide sales forecasts and pipeline reporting in HubSpot; influence the product roadmap; and represent Nabla at industry events.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Lead Tilt’s product design function across consumer financial products. Set design vision and quality standards, manage and grow a team of six designers and one researcher, build research capabilities, influence product strategy, scale the design system, advocate for users, and partner with Product, Engineering, Marketing, and executive leadership. Drive AI-enabled design practices and ensure product experiences deliver measurable user and business impact.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

Serve as a customer-facing fraud and identity expert across pre-sales, onboarding, and expansion. Evaluate customer risk profiles, design fraud strategies and identity workflows, advise on risk mitigation and verification optimization, and coordinate technical, regulatory, and operational launch readiness. Lead implementation milestones, go-live planning, and cross-functional resolution of deployment bottlenecks while helping enterprise customers maximize adoption and long-term value.

Own the strategy, roadmap, and growth of Ottimate’s new product initiatives across procurement, budgeting, spend planning, and contract management. Lead customer discovery, validation, MVP development, launches, and portfolio metrics. Manage product managers and designers, oversee Design and UX, and partner with engineering, data science, sales, marketing, and customer success to deliver coherent, market-ready products.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Owns CareSource’s EDI platform vision, roadmap, backlog, and delivery. Translates healthcare, regulatory, and integration requirements into technical specifications; partners with engineering, architecture, QA, DevOps, trading partners, and business teams; coordinates releases and Agile delivery; monitors transaction performance, defects, SLAs, and platform telemetry; drives data quality, observability, automation, compliance, documentation, and continuous improvement across EDI capabilities.

Leads the design and implementation of enterprise observability, AIOps, distributed architectures, cloud platforms, and software engineering solutions. Evaluates emerging technologies, guides architecture roadmaps, manages vendor governance, leads modernization and migration initiatives, and develops automation to improve system reliability and reduce detection and resolution times. The role also manages and coaches geographically distributed engineering teams while partnering with executive, cloud, SRE, and platform stakeholders.

Build and scale an international robotics data acquisition network across emerging markets. Source venues and strategic local agencies, optimize lead-generation funnels, evaluate deployment environments, coordinate hardware installation and calibration, and establish reliable data pipelines. Manage vendor training, SLAs, KPIs, verification, and anti-fraud controls while translating robotics and embodied AI data requirements into field operations. Requires international growth or field operations experience, partner management, technical data fluency, language skills, and frequent travel.

Leads the development and ownership of Cedar’s production machine learning personalization engine. Responsibilities include ML modeling, MLOps, feature pipelines, deployment, inference, monitoring, feedback-loop optimization, compliance and fairness guardrails, technical leadership, code reviews, cross-functional collaboration, and management of ML engineers. The role requires hands-on contributions to scalable ML systems serving millions of healthcare transactions while guiding roadmap execution and model improvement.

Manage and optimize hybrid Azure, AWS, and on-premises infrastructure across its full lifecycle. Responsibilities include cloud administration, automation with PowerShell, Python, and Terraform, AIOps monitoring, Docker and Kubernetes management, identity and security enforcement, disaster recovery, VMware and Windows Server administration, and CI/CD enablement. The role also collaborates with data science teams on machine learning and intelligent-agent infrastructure optimization while mentoring junior staff.

The Data Scientist will support Army network and enterprise information environment missions by analyzing complex datasets, developing statistical and machine learning models, integrating APIs and automated data pipelines, and building Palantir Foundry and Army Vantage applications. The role includes creating executive visualizations, applying AIP tools, supporting secure Army and DoD data environments, and translating technical findings into actionable recommendations for senior leadership.

Develop and maintain large housing datasets and infrastructure for US housing policy research. Responsibilities include data acquisition, cleaning, analysis, modeling, automation, visualization, dashboard development, web scraping, API use, quality control, and publication. Senior responsibilities add predictive modeling, machine learning, workflow supervision, project management, and establishing data-management best practices.

Manage and administer Mission Assurance databases while ensuring data integrity, security, availability, and performance. Support data collection, normalization, governance, quality control, repository integration, stewardship, and analytical workflow sustainment. Integrate assessment data with mission mapping, risk management, and criticality analysis to identify trends, vulnerabilities, dependencies, and enterprise-level risks. Apply advanced analytical techniques, including artificial intelligence and machine learning, to support Air Force mission assurance activities.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

Drive revenue growth across Federal Civilian agencies by acquiring new accounts, expanding existing contracts, managing sales cycles, building executive relationships, and achieving quotas. Develop sales strategies, forecasts, pipelines, and partner ecosystems with systems integrators and resellers. Present networking, security, AI, and data security solutions while communicating business value, ROI, and operational impact to technical and executive stakeholders.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Leads data science and advanced analytics initiatives for Auto Claims by developing, evaluating, and implementing machine learning and predictive modeling solutions. Integrates structured and unstructured data, researches emerging techniques, communicates model results to stakeholders, and manages moderately complex projects. Provides initial mentoring and development support to junior data scientists while helping improve business decisions and outcomes.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.