Top Senior Machine Learning Jobs in Washington, DC

Lead data science initiatives involving entity and identity resolution, knowledge graphs, information extraction, search, predictive modeling, and AI-enabled discovery. Design data architectures and machine learning workflows, establish model development and monitoring standards, guide scalable data products, evaluate emerging technologies, and brief senior leadership on AI strategy, technical risks, and investment opportunities.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Owns the strategy, roadmap, development, and adoption of financial data products and APIs. Partners with data science and engineering to integrate predictive and machine-learning models into scalable, trustworthy capabilities. Defines quality metrics, requirements, documentation, and support models while ensuring privacy, security, regulatory compliance, and downstream adoption across consumer and business banking products.

Leads enterprise AI platform strategy, architecture, engineering, governance, and operations across machine learning, generative AI, agents, data, and business intelligence. Owns platform standards, reusable services, lifecycle management, security, compliance, reliability, vendor commitments, budgets, and consumption. Directs production-scale AI enablement, establishes responsible AI controls, and manages engineering teams, staffing, operating plans, and executive stakeholder alignment.

Drafts, reviews, and negotiates complex data, content, licensing, distribution, vendor, and supplier agreements. Advises internal teams on intellectual property, data and content rights, AI and machine learning use cases, privacy, regulatory compliance, and commercial disputes. Partners with Content, Sales, Marketing, Finance, and Product teams; develops policies, playbooks, and project plans; monitors evolving AI and data regulations; and provides broader corporate legal support.

Provides advanced technical support for robotic systems during commissioning and 24/7 customer operations. Diagnoses hardware, software, controls, Linux, network, and automation failures; analyzes logs and telemetry; develops integrations and support workflows; leads major incidents; coordinates engineering, field service, and customer teams; manages cases and corrective actions; validates AI-assisted diagnostics; and mentors engineers while improving system reliability and recovery times.

Provides statistical leadership for Medical Affairs, real-world evidence, comparative effectiveness, HTA, and HEOR activities. Designs and analyzes observational, clinical, Phase IV, and indirect treatment comparison studies; evaluates data quality, bias, uncertainty, and applicability; supports evidence-generation strategies, reimbursement submissions, publications, and scientific communications. Collaborates with statistics, epidemiology, RWE, medical, and external scientific teams to translate complex analyses into actionable conclusions.

The Senior Business Development Representative will generate enterprise sales pipeline through account research, lead identification, outbound calls, emails, and social outreach. The role qualifies opportunities, maintains accurate Salesforce records, represents Rasa’s AI platform, collaborates with sales and marketing teams, and works toward exceeding quotas. This fully remote position requires strong communication, organization, customer empathy, autonomy, and comfort working in a fast-paced startup environment.

Own the strategy and roadmap for Constructor’s search quality and Product Discovery Engine. Drive measurable improvements in relevance, conversion, revenue per visit, and GMV across retail verticals. Partner with ML, data science, and engineering teams to productionize state-of-the-art retrieval, ranking, query understanding, personalization, and LLM capabilities. Establish evaluation frameworks, investigate quality issues, align multiple teams, and expand into chapter-wide product leadership.

Leads an Indo-Pacific intelligence team and manages contract staffing, finances, schedules, technical performance, reporting, and subcontractors. Directs and mentors analysts, oversees intelligence production, enforces analytic standards, coordinates with government customers and senior stakeholders, and supports staffing and recruiting. The role requires expertise in Defense Intelligence Enterprise processes, all-source analysis, Indo-Pacific threats, ICD 203/206 standards, and integrating AI and ML into intelligence workflows.

Owns operations for Optain’s nationwide network of contracted optometrists and ophthalmologists. Responsibilities include creating provider policies and SOPs, running quality and performance programs, modeling capacity, managing recruitment, handling complaints and urgent findings workflows, and preparing governance decisions for the Medical Director. The role partners with Product and Engineering to automate network processes and requires experience in healthcare operations, clinician networks, clinical quality, credentialing, and multistate licensing.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Leads delivery of large-scale AWS and digital transformation programs, overseeing planning, execution, reporting, risk mitigation, quality, and customer outcomes. Manages multi-functional teams of business analysts and engineers, develops product and service roadmaps, drives continuous improvement, and resolves customer issues. The role also involves people management, recruiting, coaching, business development, stakeholder communication, and aligning program portfolios with organizational strategy.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Analyze large and complex datasets to identify trends, clean and transform data, query databases with SQL, and develop dashboards and visualizations using Databricks, Tableau, Power BI, and Excel. Produce analytical reports and recommendations, apply predictive analytics when appropriate, and support agile delivery through user story documentation and testing.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Own the end-to-end product strategy and lifecycle for AI research products, from model development and user research through launch, pricing, and go-to-market. Partner with research, engineering, design, and business teams to define requirements, ship products, analyze metrics, run experiments, and prioritize features. The role requires strong technical or quantitative foundations, product management experience, SQL and Python fluency, knowledge of machine learning, and experience taking products from zero to one.

Lead the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable model-serving infrastructure. Establish engineering standards, guide complex initiatives, improve reliability and cloud cost efficiency, and partner across engineering and data teams. The role also includes mentoring engineers, managing production operations, and developing AI-assisted automation tools.

Build, fine-tune, evaluate, and deploy computer vision and multimodal machine learning models in production. Develop scalable data pipelines, evaluation frameworks, real-time processing systems, and cloud infrastructure with monitoring and automated scaling. Improve image processing, barcode decoding, and OCR capabilities while partnering with engineering and fulfillment teams. Contribute through code reviews, documentation, testing, technical leadership, and mentorship.

Designs, builds, and deploys production machine learning systems for search, ranking, discovery, recommendations, and personalization. Responsibilities include semantic and hybrid retrieval, embeddings, data pipelines, search infrastructure, evaluation, monitoring, experimentation, performance optimization, and privacy-aware access controls. The role partners with product and engineering teams to integrate intelligent capabilities across healthcare, commerce, clinical, pharmacy, patient, and merchant applications, while mentoring engineers and owning initiatives through deployment and continuous improvement.

Lead the design and production operation of Navanta’s AI platform, including RAG, retrieval, model serving, MCP tool orchestration, text-to-SQL, evaluation, and guardrails. Build auditable, secure AI systems with PII protection, prompt-injection defenses, grounded responses, and governed metric selection. Partner with data, platform, and product teams while optimizing model quality, latency, cost, and reliability for regulated banking environments.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Lead architecture and technical strategy for a remote engineering organization building a React, Node.js, PostgreSQL, and AWS platform. Mentor engineers, establish engineering standards, drive JavaScript-to-TypeScript migration, design ETL and AI/LLM integration systems, resolve technical debt, and oversee technical design reviews, production support, incident response, hiring, and delivery. Partner with product, clinical, and business stakeholders to build scalable, reliable software.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Own projects from prototyping through production, conduct experiments, explore large-scale data, and collaborate with product, engineering, analysts, and account teams to improve attribution and marketing ROI.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.