Top Senior Machine Learning Jobs in Washington, DC

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

The Commercial Legal Director will lead complex technology and commercial transactions, negotiate SaaS, licensing, partnership, marketing, and services agreements, and develop scalable legal contracting standards. The role advises teams on AI, intellectual property, privacy, security, and regulatory issues; improves contracting workflows using Ironclad and AI-enabled tools; and partners with senior stakeholders across product, engineering, finance, marketing, and people operations.

Own analytics for Root’s Customer Experience domain and policyholder retention. Develop metrics, analytical frameworks, experimentation systems, and strategic recommendations. Partner cross-functionally with Product, Engineering, Data Science, and Operations to shape roadmaps, evaluate customer initiatives, quantify business impact, and influence senior leadership decisions through rigorous analysis and clear communication.

Build and scale Pulse’s technology partner ecosystem by recruiting and signing partners, structuring integrations and commercial agreements, developing joint go-to-market and co-sell programs, and driving partner-sourced pipeline, revenue, and API usage. The role also manages partner onboarding, enablement, deal attribution, forecasting, technical validation, and feedback to product and engineering teams.

Lead and grow a global engineering team building endpoint visibility, prevention, and on-device ML across Windows, macOS, and Linux. Own roadmap and delivery, direct model development and telemetry, drive cross-platform protection parity, coordinate with product and partner teams, and represent the team externally through research and conferences.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Own the enterprise AI strategy, roadmap, governance, investment priorities, and measurement framework. Identify and prototype high-value AI use cases, guide pilots into production, establish responsible-AI standards for sensitive data, and communicate outcomes to executives. Build adoption through enablement and reusable practices while coordinating technology, business, legal, security, data, and vendor partners. Lead the emerging AI strategy function and hold teams accountable for delivery and value realization.

Advise Product, Engineering, Security, and business teams on software, SaaS, cloud, privacy, cybersecurity, AI, intellectual property, and emerging technology regulations. Guide products throughout their lifecycle, assess legal and regulatory risks, draft agreements and privacy documentation, support privacy-by-design, monitor global regulations, and develop scalable legal processes, playbooks, policies, and training.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Drive AI-enabled product strategy and execution by identifying customer opportunities, defining requirements, and partnering with engineering, design, data, sales, marketing, and customers. Lead development of products using LLMs, generative AI, RAG, and intelligent agents; validate roadmap priorities, communicate product vision, support launches, and improve feature adoption. Apply AI tools to accelerate product specifications, workflows, and software delivery.

Designs, implements, and maintains cloud security controls across Microsoft 365, GCC High, Azure, AWS, and hybrid environments. Leads cloud and AI security architecture, threat modeling, infrastructure-as-code automation, security testing, posture management, and incident support. Develops secure patterns for generative AI, machine learning, copilots, agents, and CUI workloads. Translates CMMC, NIST, DFARS, ITAR, ISO 27001, GDPR, and related requirements into technical controls, evidence, and risk mitigations while mentoring engineers and guiding cross-functional teams.

Implements and maintains cloud security controls across Microsoft 365 GCC High, Azure, AWS, and hybrid environments. Administers cloud security platforms, integrates telemetry, supports vulnerability remediation, and validates controls against CMMC, NIST, DFARS, ITAR, ISO 27001, and other requirements. Secures generative AI, machine-learning, copilot, RAG, and agentic systems through access controls, sandboxing, logging, and execution safeguards. Performs risk assessments, control testing, audit evidence collection, SOC escalation support, automation, documentation, and occasional off-hours incident support.

Designs end-to-end architectures and high-level designs for web security products and features, spanning configuration, reporting, runtime systems, analytics, and attack detection. Collaborates with product management and cross-functional teams to define requirements, guides engineering implementation, presents designs, and contributes security product expertise and technical roadmap direction.

Leads customer engagements and large-scale analytics projects from inception through delivery, ensuring measurable business value, quality, budgets, and timelines. Builds customer relationships, translates needs into analytics solutions, supports renewals and expansions, and coordinates with product, engineering, and data science teams. Also supports solution architecture, pre-sales demonstrations, proof-of-concept implementations, technical use-case development, and customer conferences. Presents complex insights to technical and nontechnical stakeholders.

Designs and implements AI solution architectures and strategies addressing client business needs and ethical considerations. Develops innovative solutions using AI, machine learning, deep learning, computer vision, NLP, and agentic AI. Leads technology assessments, evaluates alternatives, develops requirements, conducts independent research, translates complex technical topics for broad audiences, and mentors team members. Requires Python programming, modern software design knowledge, a STEM degree, and Secret clearance eligibility.

Leads enterprise AI software deployments for federal and defense customers, managing client relationships, project delivery, training, onboarding, feedback integration, and cross-functional execution. Builds and manages delivery teams, partners with developers and leadership, supports proposals and business development, and maintains executive stakeholder relationships. The role requires expertise in PPBE, resource planning, POM cycles, analytics, AI methods, data integration, and enterprise solution delivery, with a focus on completing projects and expanding customer adoption.

Lead technical strategy for financial services customers by designing scalable data, AI, streaming, and analytics architectures. Own customer engagements from discovery through production deployment, advise senior technical stakeholders, drive Databricks adoption, lead competitive technical wins, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL coding, distributed data systems expertise, public cloud deployment experience, and the ability to translate complex architectures into business value.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Lead technical onboarding and enablement for contact center partners, architecting scalable Zendesk, Amazon Connect, and AWS solutions. Provide co-selling support, technical strategy, solution design, demonstrations, proof-of-concepts, training, documentation, governance, and mentorship. Collaborate with partners, sales, engineering, and vendors to drive successful implementations, security compliance, innovation, and adoption of AI, automation, and omnichannel contact center technologies.

Leads technical solution architecture for federal Base Operations Support opportunities, integrating facility maintenance, sustainment, CMMS, asset management, reliability, energy management, workforce, supply chain, and emerging technologies. Develops executable technical approaches, staffing models, performance metrics, transition plans, cost inputs, and proposal content. Engages customers and multidisciplinary teams, supports capture and proposal activities, evaluates operational risks and technologies, and designs solutions that improve facility reliability, mission readiness, efficiency, and lifecycle affordability.

Develops data science solutions supporting Army and Department of Defense platforms. Responsibilities include API integration, automated data pipelines, Palantir Foundry and Army Vantage development, data architecture, executive visualizations, statistical modeling, predictive machine learning, neural networks, and LLM customization. The role translates complex datasets into actionable insights for senior leaders and requires collaboration across technical and military stakeholders.

Leads quantitative and qualitative market research on international development, AI adoption, institutional demand, funding, procurement, competition, and commercial opportunities. Evaluates AI and data service applications, develops market assessments and strategic recommendations, manages research initiatives, prepares executive presentations, and collaborates with leadership, technical, operational, and business development teams to support market entry and growth.

Own Fetch’s on-platform advertising measurement strategy, including event taxonomies, attribution, incrementality, reporting, and data pipeline standardization. Lead migration to standardized ad event infrastructure, define measurement methodologies such as multi-touch attribution and geo lift, and partner with engineering, data science, advertisers, and third-party measurement providers. Establish scalable measurement standards and ensure new ad products are instrumented and attributable from launch.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Leads enterprise Key Drivers Analytics and an Associate Director team delivering advanced analytics across pharmaceutical business priorities. Owns analytics roadmaps, methodologies, governance, modeling standards, talent deployment, and globally distributed delivery. Directs multivariate analyses, segmentation, diagnostics, scenario modeling, validation, insight generation, and executive recommendations. Partners cross-functionally to translate evidence into measurable business actions while ensuring reproducibility, compliance, responsible AI, and scalable analytical solutions.