Top Machine Learning Jobs in Richmond, VA

The role involves leading AI/ML Engineering, establishing technology vision, managing a large team, and ensuring quality AI solutions. Key responsibilities include architecting automation pipelines and fostering a product engineering culture.

Lead development and operationalization of pricing algorithms to value vehicles, deliver $15-30M/year in incremental value, build and manage a 4-8 person data science team, develop AI/ML models to autonomously price vehicles, run experiments, and implement model monitoring and incident response with cross-functional partners.

Lead development and deployment of predictive customer progression models across the full lifecycle. Analyze large, complex datasets, design experiments (A/B tests), collaborate with Product/Engineering/Operations, and mentor a team of data scientists while driving scalable modeling infrastructure and communicating insights to influence business strategy.

Lead development and improvement of vehicle pricing models by applying machine learning and statistical techniques, feature engineering, and controlled experiments; present insights to senior leaders; research modeling innovations and influence the team's modeling roadmap; communicate model behavior to stakeholders.

Lead development of optimization and AI-driven logistics planning to move millions of vehicles efficiently. Build and manage a team of data scientists, design and implement planning models and software, collaborate cross-functionally, run experiments, and deliver significant cost and service improvements.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

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.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Lead a multi-year finance transformation program to standardize processes, modernize systems, improve data and analytics, and strengthen financial controls. Partner with cross-functional leaders to drive ERP optimization, process redesign (P2P, O2C, F2P, cost accounting), change management, and talent development to improve financial performance and scalability.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Lead the development of pricing algorithms for vehicle valuation at CarMax, managing a team of data scientists to implement strategies and maintain optimal pricing models.

Lead AI/ML-focused information security for enterprise agentic capabilities. Advise projects and CSOC teams on threat mitigation, compliance, automation, monitoring, and incident response. Partner with engineering and stakeholders to secure cloud, generative AI, and customer servicing platforms while driving data-driven decisions and continuous improvements.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.