Top Remote Machine Learning Jobs in Washington, DC

Develop, validate, deploy, and maintain machine learning and deep learning algorithms for biosignal-based medical devices. Responsibilities include data curation, algorithm experimentation, failure analysis, production support, documentation, testing, CI/CD, and stakeholder presentations. The role collaborates with data scientists, neuroscientists, engineers, clinicians, and clients to advance algorithms supporting sleep, neurological, psychiatric, and precision medicine applications.

Leads product strategy, roadmap, and execution for threat intelligence, threat hunting, and detection capabilities. Partners with engineering, architecture, security research, executive, sales, and SOC teams to drive innovation, product launches, OKRs, portfolio prioritization, and customer security outcomes. Requires deep cybersecurity expertise and extensive senior product management experience, with strong knowledge of emerging threats, AI applications, and enterprise security products.

Leads strategy and execution for data products powering threat intelligence solutions. Uses customer insights, big-data analysis, and competitive research to prioritize opportunities, refine API-first products, and integrate data into UI, AI, and third-party workflows. Collaborates with engineering, marketing, sales, and solutions engineering from ideation through launch, influencing stakeholders and executives through data-driven analysis. Requires substantial cybersecurity or data product management experience and expertise in threat intelligence, APIs, analytics, and customer ecosystems.

Leads ExtraHop’s product marketing function, including team leadership, portfolio positioning, messaging, product launches, competitive analysis, sales enablement, content development, integrated campaigns, and executive-level customer and industry engagement. The role requires deep cybersecurity and enterprise technology expertise, strong storytelling and technical communication skills, and collaboration across product, sales, marketing, and partner teams.

Build and lead Precision Neuroscience’s partnership function across the life sciences and healthcare ecosystem. The role owns strategic partnership pipelines, structures and negotiates complex technology and scientific collaboration agreements, advises leadership on deal strategy, translates technical work into commercial narratives, and establishes partnership processes for an early-stage neurotechnology company.

Own a broad territory focused on acquiring net-new enterprise customers for SingleStore’s AI and data platform. Build self-sourced pipeline through outbound, account-based, partner, and market-education motions; engage executive and technical stakeholders; develop ROI and TCO cases; navigate complex technical, security, legal, procurement, and negotiation processes; partner with engineers to establish production workloads; and manage forecasting, qualification, CRM hygiene, and new-logo performance.

Drives growth by identifying strategic partnerships with enterprise AI and machine learning companies. Develops business development strategies, builds relationships with senior decision-makers, negotiates complex deals, and collaborates with internal teams to implement partnerships. Requires strong AI/ML industry knowledge, executive communication skills, strategic thinking, and adaptability in a fast-paced startup environment.

Leads cross-functional programs that improve products, customer support, and value delivery. Owns execution, coordinates engineering and operations teams, establishes scalable processes, uses data-driven insights to improve customer models, scopes and sequences major initiatives, and communicates progress to stakeholders. The role requires program management experience, strong communication and influence skills, comfort with ambiguity, rapid execution in scaling environments, and interest in AI systems.

Manages strategic client accounts for an AI data platform, serving as the primary customer contact and trusted advisor. Oversees data projects, SLAs, quality, and deliverables while coordinating engineering, operations, and project teams. Builds strong client relationships, addresses concerns, translates AI and NLP requirements into actionable plans, and ensures solutions meet customer goals.

Lead finance-focused AI training, evaluation, and data programs across financial analysis, equity research, credit assessment, regulatory compliance, and quantitative modeling. Serve as the finance subject-matter expert, establish quality frameworks, coordinate cross-functional teams, manage timelines, and translate financial expertise for AI/ML teams and financial-services clients.

Conduct hands-on research with frontier AI labs to evaluate model behavior, identify failure modes, and design interventions that improve reliability. Responsibilities include designing evaluations, building data pipelines and fine-tuning workflows, collaborating with internal and external stakeholders, and addressing challenges involving long-context reasoning, tool use, and real-time feedback loops.

Lead customer-centric product development and strategic initiatives for an AI platform. Responsibilities include understanding customer pain points, overseeing product and operations execution, managing accounts, building scalable systems and processes, coordinating with engineering, analyzing performance data, and driving product insights through market and competitive research.

Forward Deployed Engineers embed with customers to identify operational problems, analyze imperfect data, rapidly prototype and deploy AI-powered solutions, and drive adoption. Responsibilities include building pipelines, models, dashboards, optimizers, agents, and operational applications; collaborating with stakeholders; training users; improving platform capabilities; and supporting follow-on customer opportunities. The role requires technical execution, product thinking, client partnership, commercial awareness, and significant travel.

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.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Own machine learning models end to end, from data preparation and training through evaluation and production deployment. Build pipelines for large multimodal datasets including video, sensor streams, and language. Collaborate with engineering and computer vision teams, rapidly prototype solutions, apply relevant research, and continuously improve production models and tooling.

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.

Develop and launch scalable, user-facing features for AI-powered video and multimedia tools. Architect end-to-end solutions across backend and frontend systems, ensure quality and reliability, collaborate with design, product, and ML teams, and mentor engineers. The role involves rapid prototyping, experimentation, technical leadership, and shaping Pika’s core creative product.

Conduct applied AI and machine learning research for semiconductor testing. Define ambiguous technical problems, develop measurable evaluation methods, build and fine-tune models, and create RAG, knowledge-graph, and agent systems. Translate unstructured tester documentation and protocols into production solutions, collaborate with engineering and domain experts, and deploy tools that improve workflows. The role also requires monitoring emerging AI technologies and proposing new research directions.

Lead the technical direction and hands-on development of ML and operations research systems powering delivery dispatch. Build pricing, quote selection, forecasting, routing, batching, fleet recommendation, LLM, optimization, evaluation, and MLOps capabilities. Translate operational constraints into reliable models and solver formulations, establish production ML standards, and mentor engineers. The role partners with Product and leadership to deliver measurable improvements in revenue, margin, utilization, conversion, and delivery reliability.

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.

Manage strategic enterprise accounts, building executive relationships, developing multi-year account plans, driving retention, expansion, upsell and cross-sell opportunities, and closing complex technology deals. Coordinate cross-functional Basis teams, lead executive business reviews, align customer priorities with company objectives, and represent Basis at customer and industry events.

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.