Top Senior Machine Learning Jobs in Washington, DC

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own post-sale relationships with referral partners, driving adoption, retention, renewals, expansion, and revenue growth. Serve as a strategic advisor, lead business reviews, resolve escalations, coordinate cross-functional teams, enable product usage, and advocate for partner needs. Maintain partner records and report on satisfaction, renewal, and revenue metrics while developing success strategies and playbooks.

Designs, architects, and leads delivery of enterprise applications, APIs, platforms, and AI-enabled solutions. Builds agentic workflows, LLM and RAG applications, integrations, data pipelines, and full-stack services across Azure and AWS. Establishes secure engineering, testing, CI/CD, observability, and reliability practices while addressing AI safety and governance. Partners with attorneys, product owners, AI and data teams, and executives to translate complex workflows into scalable solutions, lead architecture, mentor engineers, and drive initiatives from prototype through production.

Provides strategic legal counsel on global privacy, data governance, cybersecurity, and AI regulations. Leads enterprise data governance frameworks, classification, retention, access, sharing, deidentification, and responsible data-use initiatives. Drafts data processing, data sharing, AI, privacy, and security agreements; partners with product, engineering, security, compliance, and government affairs teams; develops policies and training; and leads regulatory inquiries, audits, investigations, and litigation readiness.

Provides pre-sales technical leadership for AI security solutions, including technical discovery, architecture reviews, demonstrations, proof-of-concepts, deployment support, partner enablement, and customer training. The role supports sales strategy, develops demo environments, enables regional sales engineers, manages technical handoffs, and builds relationships with enterprise AI and security leaders. Requires AI security expertise, cloud deployment experience, Docker and Kubernetes knowledge, and travel up to 50%.

Leads go-to-market strategy and quota-carrying enterprise sales for Fortinet’s AI security solutions. Responsibilities include building pipeline, closing complex deals, conducting executive presentations and demonstrations, consulting on AI security architecture, training account teams and partners, and developing strategic customer relationships. The role serves as an AI security evangelist while collaborating with sales, marketing, product, and channel teams to drive adoption and business outcomes.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Develops and applies data science models to analyze large datasets, solve complex data and storage challenges, improve search processes, validate data quality, and deliver actionable recommendations. The role emphasizes modeling and programming over visualization and analytics, using Python, Spark, R, Databricks, NLP, LLMs, machine learning, predictive modeling, and AWS. Requires an active Top Secret/SCI-eligible clearance and potential CI polygraph eligibility.

Leads talent acquisition strategy for specialized AI, machine learning, robotics, automation, and advanced research roles. Sources and closes highly technical candidates, advises senior leaders on talent markets and compensation, mentors recruiters, builds research and industry relationships, and improves recruiting processes using analytics, recruiting technology, and Lean methods.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Lead the design and development of Azure Compute Fleet capabilities that orchestrate Virtual Machine Scale Sets across regions. Build distributed control-plane services, APIs, and workflows for capacity placement, scaling, failover, and lifecycle management. Collaborate across Azure engineering teams, drive AI-enabled engineering improvements, participate in on-call operations, and improve reliability, observability, performance, and recovery for hyperscale cloud services.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Develop and deploy UiPath-based RPA solutions for internal and external clients. Responsibilities include requirements gathering, process documentation, automation development, testing, deployment, queue management, scheduling, exception handling, debugging, governance, and Center of Excellence support. The role also involves business process analysis, system design, process optimization, change management, tool selection, and collaboration with developers, analysts, testers, and consulting teams.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Build and deploy AI-enabled solutions for Marketplace operations and commercial challenges. Identify opportunities, frame ambiguous business problems, develop prototypes using enterprise data and AI/LLM tools, validate business value, and transition successful solutions into production. Partner with business, product, technology, and data teams; drive adoption, measure outcomes, coordinate initiatives, mentor team members, and enhance reusable platforms and frameworks.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Drive enterprise SaaS sales from prospecting through close, expanding adoption among strategic accounts. Engage business and technical decision-makers, including C-suite stakeholders, to address fraud challenges using Sift’s machine learning platform. Build consultative business cases, articulate platform differentiation, collaborate cross-functionally, and help shape sales strategy and growth. Requires strong organizational skills, adaptability, and experience managing complex B2B sales cycles.

Build and maintain a full-stack sewer inspection analysis platform using React, TypeScript, Node.js, PostgreSQL/PostGIS, AWS, computer vision, geospatial analysis, and Temporal.io. Responsibilities include developing interactive dashboards, scalable APIs, video-processing workflows, mapping and risk-assessment tools, microservices, cloud infrastructure, and automated testing and deployment pipelines.

Leads the team building LTK’s foundational AI platform, including shared models, services, evaluation frameworks, and production LLM capabilities. Responsibilities include setting the AI roadmap, enabling internal engineering and product teams, overseeing deployment and operational performance, managing quality and cost, and establishing evaluation and monitoring practices. The role also involves hiring, coaching, performance management, and developing a high-performing data science and machine learning organization.

Drive enterprise SaaS sales across EMEA by prospecting AI and machine learning stakeholders, conducting discovery, presenting Arize’s platform, managing proof-of-concept engagements, navigating complex sales cycles, closing new business, driving adoption, and expanding customer accounts. The role requires full-cycle enterprise sales expertise, strong communication, independent execution, account research, and relationship-building within the AI/ML community.

Lead actuarial pricing for Lemonade’s car insurance products, including state rating plans, rate indications, predictive modeling, pricing strategy, and rate filing support. Partner with Data Science to integrate machine learning models, monitor pricing and underwriting results, assess competitors, and expand into new states. Manage and develop a team of actuaries while supporting scalable pricing tools and data-driven decision-making.

Leads the global Customer Success organization and owns post-sale strategy, retention, expansion, adoption, customer health, and recurring revenue performance. Builds scalable teams, processes, lifecycle frameworks, and operating systems supporting rapid international growth. Integrates AI, predictive analytics, and automation into Customer Success operations while partnering with Sales, Product, Engineering, Marketing, and Operations. Manages executive relationships with strategic customers, drives forecasting accuracy, and ensures measurable customer outcomes across distributed regions.

Lead complex actuarial pricing work for personal lines insurance, including rate indications, regulatory filings, pricing analyses, predictive-model implementation, and state expansion. Develop automation and scalable filing tools, communicate recommendations to technical and executive stakeholders, manage regulatory risks, and coach other actuaries. Partner cross-functionally with Product, Regulatory, Data Science, Engineering, and Finance teams while maintaining analytical rigor and compliance.

Lead product design for Fetch’s Receipt & Rewards experience, connecting receipt capture, account linking, recognition, matching, and rewards. Define design strategy for complex, AI-enabled systems; improve usability, accessibility, trust, and experience quality; and balance user needs, data quality, business goals, and technical constraints. Partner with Product, Engineering, Research, and Data Science, drive cross-functional alignment, shape multi-quarter initiatives, and mentor designers.