Top Senior Machine Learning Jobs in Washington, DC

Owns the commercial health and client relationships for Databricks data and AI engagements. Responsibilities include managing budgets, margins, forecasts, scope changes, executive stakeholders, steering committees, delivery transitions, business outcomes, close-outs, and follow-on opportunities. Partners with Sales, project managers, solutions architects, and technical leaders to deliver measurable client value across data engineering, lakehouse, governance, BI, and AI initiatives.

Drive the full enterprise sales cycle for data engineering, platform modernization, and AI/ML consulting services. Build pipeline through prospecting, outreach, networking, campaigns, and events; develop pursuit strategies; close complex deals; and expand strategic partnerships. Own executive client relationships, coordinate sales, delivery, customer success, solutions engineering, and marketing, while maintaining accurate forecasts and achieving revenue targets.

Own the strategy, roadmap, backlog, and delivery of GridUnity’s AI platform capabilities. Partner with customers, engineering, design, data science, sales, and leadership to validate AI use cases, define requirements, manage experiments and releases, and measure model and business outcomes. Ensure responsible AI practices including explainability, auditability, privacy, monitoring, human review, and fallback behavior. Provide technical product expertise, support customer-facing activities, and mentor other product owners.

Drive new enterprise business for Automation Anywhere’s Agentic Process Automation platform. Responsibilities include prospecting, managing sales pipelines, conducting customer presentations and demonstrations, attending industry events, leading proof-of-concept exercises and RFX responses, and coordinating solution design with technical teams. The role requires consultative enterprise software selling, quota attainment, technical communication, and collaboration with sales engineers, partners, and professional services.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Manage strategic state and local government accounts for NVIDIA’s AI and accelerated computing solutions. Own the full technology sales cycle, develop territory and account plans, build executive and technical relationships, collaborate with solution architects and channel partners, navigate public-sector procurement, forecast revenue, and achieve sales quotas. The role requires consultative selling to government organizations and travel of at least 25%.

Analyzes and transforms complex financial and operational data using SQL and Python; integrates, cleans, validates, and models datasets; develops Power BI dashboards and executive reports; supports data governance, quality assurance, federal compliance, and financial decision-making. The role also leads client meetings, documents action items, develops training materials, and supports federal financial management transformation initiatives.

Designs, refactors, tests, and deploys secure PowerShell automation frameworks for Navy Records Management modernization. Processes large datasets, optimizes analytics pipelines, develops dashboards and workflow tools, and improves script performance, scalability, and resilience. The role applies Pester testing, secure coding, role-based access controls, Agile delivery, and data science practices while collaborating with program managers, developers, and Navy stakeholders.

Own Clara’s global fraud detection strategy across markets and fraud vectors. The role develops rules, risk signals, machine learning models, automation, monitoring, and case-management capabilities; leads AI and workflow automation; establishes model governance and KPIs; identifies emerging threats; and partners with Fraud Operations, Risk, Product, Engineering, and Data Science to deliver scalable, explainable, and effective fraud controls.

Own complex AI programs from concept through model development, cloud and edge deployment, and post-launch optimization. Lead cross-functional teams across AI research, engineering, product, and hardware; manage roadmaps, dependencies, metrics, executive reporting, data lifecycles, annotation pipelines, evaluations, and vendors. The role also mentors TPMs and supports AI enablement. Experience with large-scale distributed systems, machine learning delivery, cloud platforms, technical documentation, and ambiguous requirements is required.

Develops data, AI, business intelligence, and workflow automation solutions for federal clients. Responsibilities include analyzing processes and data, gathering requirements, building dashboards and analytical applications, integrating data sources, implementing AI-enabled tools, creating prototypes, testing solutions, documenting implementations, and advising government stakeholders. The role also supports data governance, security, privacy, performance monitoring, and continuous solution improvement.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Lead growth data science initiatives by partnering with Marketing, Sales, and Customer Experience leaders. Develop analyses, experimentation frameworks, and machine learning models for acquisition, engagement, retention, monetization, targeting, segmentation, uplift modeling, and lifetime value prediction. Productionize reliable models with engineering teams, communicate insights to senior stakeholders, establish experimentation best practices, and mentor junior data scientists. The role requires strong business judgment, advanced technical expertise, and experience operating in ambiguous, zero-to-one environments.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, model benchmarking, routing, and fallback strategies. Architect scalable backend and AI platform services for LLM deployment, orchestration, monitoring, logging, and automated evaluation. Improve reliability, privacy, security, and compliance while integrating AI features into the core product and collaborating cross-functionally.

Own company-level strategic initiatives, establish measurable goals, diagnose delivery risks, and recommend resource reallocations. Lead cross-functional transformation focused on go-to-market scalability, annual planning, quarterly reprioritization, monthly business reviews, and AI-enabled operational reporting. Partner with executives and functional leaders to improve the operating model, automate KPI tracking, communicate priorities, and increase organizational velocity.

Own the full enterprise sales cycle for Neuron7.ai’s AI-powered Service Resolution Intelligence platform. Responsibilities include prospecting, executive discovery, strategic account planning, ROI development, technical evaluations, negotiation, procurement, and closing new-logo enterprise deals. The role targets high-tech, manufacturing, and medical device organizations, requiring independent pipeline generation, accurate forecasting, executive relationship management, and collaboration with solution consultants, architects, product, and customer success teams.

Own strategy and execution for a gaming-oriented B2C mobile app focused on advertising, rewards, loyalty, and commerce. Responsibilities include roadmap and backlog management, analytics and A/B testing, conversion optimization, user research, competitive analysis, product documentation, prioritization, and cross-functional collaboration with engineering, design, marketing, legal, operations, and support teams.

Provides technical leadership for architecture and evolution of large-scale distributed data platforms supporting Azure network telemetry. Designs streaming and batch systems, establishes reusable engineering patterns, drives cross-team technical strategy, and owns capabilities from design through production operations. Ensures scalability, reliability, security, performance, data quality, and operational excellence while mentoring engineers and influencing technical leaders. The role also involves architectural reviews, dependency management, risk reduction, and applying AI or machine learning approaches to operational systems.

Leads architecture and production delivery of a near-real-time observability platform for large-scale AI supercomputers. Responsibilities include telemetry ingestion, processing, storage, querying, topology and health modeling, 1P/3P data convergence, AI-assisted anomaly detection, technician dashboards, repair validation, security, reliability, cross-team technical leadership, and mentoring.

Leads advanced analytics, AI, automation, and scientific computing initiatives supporting pharmaceutical clinical development. Translates scientific and business requirements into scalable analytical products, software specifications, decision-support tools, and production automation. Partners across biometrics, clinical development, IT, and business teams; evaluates emerging AI technologies; improves regulated workflows; establishes value metrics; drives adoption and change management; and mentors technical talent.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Designs and scales AI-driven security GRC automation, including compliance evidence collection, control testing, risk assessment, audit preparation, and workflow integration. Builds scripts, bots, APIs, and AI/LLM solutions; integrates GRC platforms with security and IT systems; establishes quality controls and guardrails; monitors automation risk and performance; and partners with technical and business stakeholders to improve security compliance maturity.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.