Top Machine Learning Jobs in Washington, DC

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Lead quantitative credit analytics for a Send Now Pay Later (SNPL) product: build and maintain credit policies, pricing and NPV models, vintage analysis, portfolio monitoring, model integration/testing, and alternative data evaluation to optimize approval rates and manage portfolio health.

The AI Sales Specialist will develop sales strategies, engage clients, manage relationships, negotiate contracts, and provide insights on AI technologies to drive business value for enterprise clients.

Lead day-to-day operations and strategic execution for the IRS Cybersecurity Fraud Analytics and Monitoring program, managing multidisciplinary teams and coordinating stakeholder engagement and response activities.

As a Proposal Manager, you will manage the development and execution of high-value proposals, ensuring compliance and alignment across teams while driving strategy for successful submissions in a cross-functional role.

Lead program management for Public Sector computer vision projects, coordinating engineering, delivery, and analytics to optimize data labeling workflows, improve data quality and throughput, and deliver ML-ready datasets for national security customers.

Lead architecture and development of reliable, safety-critical software for nuclear energy systems. Provide technical leadership, integrate software with hardware and control systems, ensure quality and regulatory compliance, optimize performance, troubleshoot complex issues, and maintain thorough documentation.

Lead and build the full demand generation function: inbound, outbound-assist, ABM, field, and digital. Own pipeline metrics (MQLs, SQLs, pipeline contribution), run integrated enterprise campaigns, manage marketing tech stack (CRM, MAP, intent, analytics), and align closely with Sales on ICP, messaging, and pipeline quality.

Lead delivery of advanced analytics and investigative support for federal programs. Manage multiple concurrent projects, schedules, costs, risks, personnel, vendors, and stakeholder communications to ensure quality, timely contract-aligned deliverables.

The role involves developing AI technologies for customer support at Airbnb, leveraging ML systems to enhance customer experience through innovative solutions.

The Data Science Manager will lead and mentor a team of data scientists, drive data-driven decision-making across Stripe, and collaborate with various stakeholders. Responsibilities include managing projects, recruiting, and contributing to broader data science initiatives.

Design, build, and operate production AI platform capabilities and agentic workflows. Collaborate on architecture, write production-quality backend code, mentor developers, troubleshoot production issues, and drive adoption of reusable AI frameworks, MLOps practices, and cloud-native intelligent services across a multi-tenant SaaS dental platform.

Lead vision, strategy, and roadmap for Fetch's referral growth engine. Own end-to-end referral lifecycle, build incentives, experimentation, AI-driven personalization, and cross-functional initiatives to drive MAU growth and durable network effects.

Embed with customers to discover needs, design and implement production-grade integrations (APIs, data pipelines, event-driven workflows), deploy and troubleshoot solutions, train IT teams, and drive adoption and measurable business outcomes.

Perform data collection, cleaning, validation, and analysis on large client datasets using Python, SQL, and spreadsheets. Produce actionable insights and business narratives, automate data workflows, and support text/document annotation to generate labeled data for NLP and LLM solutions. Collaborate with senior analysts and improve internal analytical processes and tool configurations.

The Enterprise Account Executive will drive revenue growth by managing complex sales cycles, closing deals with Fortune 500 companies, and understanding both technical and business needs.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Develop and experimentally validate novel ML architectures to improve prediction and design of in‑vivo molecular interactions, extending structure‑informed generative models to causal models and integrating with deployed drug‑design workflows.

Lead technical solution development for military and Federal civilian health capture and proposal efforts, ensuring technical compliance and producing quality architecture designs.

The Customer Success Engineer will lead deployments, serve as a technical advisor, and support customer engagement, focusing on product enhancements and user experience.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.