Top Senior Machine Learning Jobs in Hampton, VA

Serve as technical lead in pre-sales for identity verification and fraud prevention. Lead discovery, workshops, POCs, demos, and solution design; advise on risk/compliance; align cross-functionally; influence product; mentor junior consultants and contribute thought leadership.

The Senior Product Architect will design security products, collaborating with product management and engineering teams, focusing on architecture and functionality for high-level product features.

Lead data quality and data management for oncology studies, overseeing DM teams, vendor/CRO management, and compliance with CDISC/ICH-GCP. Develop risk management, integrate and surveil multi-source data, advise stakeholders on data collection and standards, produce metrics and dashboards, manage project resourcing and finances, review contracts, and deliver training to ensure high-quality, regulatory-compliant clinical and real-world data.

The Vice President of AI will define and lead the AI strategy at Tempo, integrating AI capabilities into products, building teams, and ensuring ethical use of AI while driving business outcomes and customer value.

Support development of pricing arrangements, financial models, budgets and AFAs for a law firm. Produce pricing analyses, competitive market research, and performance metrics. Collaborate with attorneys, IT, and data teams to implement pricing tools (Elite 3E, Iridium), leverage AI/analytics for forecasting, and train stakeholders on pricing technology.

Lead technical customer engagements and guide cross-functional teams to design, build, and deploy enterprise-scale AI solutions. Provide architecture and program leadership for LLMs, RAG, ML, and cloud-native systems; establish engineering standards, CI/CD, observability, and AI governance; mentor engineers and support business development.

Lead customer-facing design, development, and deployment of AI-enabled software solutions. Translate stakeholder needs into scalable architectures, build production-quality code and APIs, implement CI/CD and observability, and mentor engineering teams. Drive solution discovery, technical leadership across projects, and adoption of modern AI/ML and DevOps practices to deliver mission-critical outcomes.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.

The Director of AI will lead AI and machine learning initiatives, develop AI architecture for humanoid robots, oversee production-ready AI systems, and manage team growth and budget.

The Senior Machine Learning Engineer will design, prototype, build AI systems, evaluate ML models, and improve Vectara's AI Agents with a focus on reliability and performance.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Lead the vision and strategy for the Commerce Intelligence Platform, transforming data assets into scalable, revenue-generating products, fostering cross-functional collaboration and driving market growth.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead the Performance ML team at MNTN, driving strategy and deployment of predictive models for ad technology, while managing, mentoring, and scaling engineering teams.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

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 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.