Top Senior Machine Learning Jobs in Washington, DC

Lead manufacturing process improvements for precision optical production: analyze data, reduce cycle time and costs, design/qualify tooling and automation, apply Lean/Six Sigma and AI-enabled analytics, maintain process documentation, partner cross-functionally, drive CAPEX and continuous improvement projects, and train production staff.

Seeking a Senior Full Stack .NET Developer to work on a social good project, with strong skills in C#, AngularJS, design, and UX. Requires 5+ years of experience.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead complex analytics projects for DoD/intelligence clients using analytic methodologies, knowledge graphs, ontologies, and semantic web standards. Design data models, enable semantic retrieval and reasoning, ensure interoperability across data sources, guide teams, and provide quantitative and qualitative analysis to senior leaders.

Build and operate production machine learning systems focused on multilingual NLP, including entity linking, classification, event extraction, deduplication, and narrative analysis. Develop GPU-accelerated workflows, fine-tune transformer models, and create LLM features such as RAG pipelines and agents on AWS Bedrock. Establish evaluation frameworks, integrate ML services into Django and event-driven platforms, lead technical architecture discussions, and mentor team members.

Lead document-vision research and production systems for multimodal legal evidence understanding. Define evaluation standards, frame hard problems, design and ship models, run efficacy studies and monitoring, mentor senior scientists, and advise leadership on scientific direction.

Lead and grow an applied science team building reliable, production-ready generative AI systems for legal use. Own end-to-end AI system readiness, set technical direction, perform evaluations and error analysis, partner cross-functionally to move work to scale, hire and coach staff, and represent the team externally.

Drive Midwest SLED revenue by selling analytics-driven SaaS identity and fraud solutions. Build pipeline, manage full sales cycle, develop partnerships/channels, collaborate with product, forecast accurately, and travel up to 50% to close deals and ensure customer success.

Lead end-to-end deployments of AI for hotel revenue teams: integrate PMS/CRS/RMS, ship ML/optimization models and agentic workflows, validate recommendations with revenue managers, define metrics, drive adoption, and hire/mentor the forward deployed engineering team.

Lead strategy, roadmap, and lifecycle for payer analytics and automation products. Translate market, customer, regulatory, and data insights into product requirements, drive cross-functional delivery with Engineering and Data Science, support go-to-market and adoption, and measure product performance to inform investment and portfolio decisions. Build product practices and partner with stakeholders across sales, customer success, clinical, actuarial, and executive teams.

Lead Growth Engineering architecture and implementation: build Go-based data pipelines, audit/replace martech, create experimentation infrastructure, ship full-stack Go/TypeScript features, productionize ML, review team work, and apply AI-assisted development daily.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Set the technical direction for machine learning at Payabli by maturing production risk models and launching new models across the payments lifecycle. Establish experimentation, monitoring, drift detection, benchmarking, retraining, and incident-response foundations. Translate payments challenges into modeling opportunities, connect model performance to business outcomes, influence the ML roadmap, and mentor engineers while partnering with product, engineering, and risk operations.

Design, build, deploy, and operate production machine learning systems for personalization, recommendations, optimization, decisioning, experimentation, and measurement. Build reusable ML platforms, pipelines, and tooling; manage model monitoring, quality, drift detection, retraining, governance, and reliability. Partner with data scientists, engineers, and product teams to productionize models, make architectural decisions, and provide technical leadership through mentorship, reviews, and engineering standards.

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 design and scaling of a unified fraud and risk platform. Own roadmap, fraud metrics, SQL-based analysis, A/B testing, real-time detection models, and cross-functional delivery to balance customer experience and risk mitigation.

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

Lead end-to-end delivery of LOIS by owning the master program plan, coordinating cross-functional teams (engineering, product, design, ML, data, SRE, operations), resolving blockers and dependencies, establishing lightweight delivery infrastructure and tooling, partnering on release planning, and maintaining transparent status, risks, and accountability to deliver complex features to customers.

Drive new business and expand accounts by selling AI-driven services and software to engineering and manufacturing customers. Manage full sales cycle from prospecting to close, align solutions to measurable ROI, collaborate with technical and marketing teams, and meet revenue targets while attending industry events and maintaining CRM pipeline accuracy.

Design, optimize, and scale ClickUp's search infrastructure using OpenSearch/ElasticSearch. Improve relevance, speed, and real-time indexing; build vector/semantic search capabilities; create measurement frameworks; integrate search with AI and product features; troubleshoot at scale; and mentor junior engineers to maintain high search quality under heavy load.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.

The Senior Backend Data Engineer will extend data ingestion and processing pipelines, build APIs and backend services, and provide technical leadership.

Lead Tekmetric's API product strategy and roadmap for the SaaS platform. Own API lifecycle (contracts, versioning, documentation), drive developer experience and adoption, identify integration opportunities, lead generative AI initiatives, perform user research and analytics-driven prioritization, collaborate across design/engineering/sales, train internal teams, and measure business impact.

Lead planning, execution, and optimization of cross-functional hardware and software projects for robot development, manufacturing, testing, upgrades, and deployments. Coordinate mechanical, electrical, embedded, systems, manufacturing, and operations teams, define scope and timelines, mitigate risks, implement process improvements, run sprints, and maintain project documentation and stakeholder communication.

Lead HR transformation engagements focusing on embedding AI and automation. Manage project delivery, develop client relationships, and mentor junior consultants.