Top Machine Learning Jobs in Richmond, VA

Lead end-to-end delivery of AI and ML services within Managed Services. Manage project planning, budgeting, execution, and quality. Mentor and develop team members, cultivate client relationships, implement model monitoring and operationalization practices, and collaborate with architects and engineers to ensure compliant, reusable AI solutions.

Lead development and delivery of GenAI/AI products for Commercial Banking. Define product specs, manage roadmaps, prioritize features, collaborate with data scientists, engineers, and cross-functional stakeholders, and serve as PM for a cross-functional pod to drive iterative quarterly delivery.

Lead the vision and roadmap for a document intelligence platform, designing taxonomy and ontology, migrating to a graph-based architecture, enabling ML-driven extraction and chat-with-document experiences, and partnering across business lines to deliver compliant, discoverable, high-impact document products.

Lead and scale engineering teams to transform the network into an Intent-Based, Infrastructure-as-Code platform. Drive automation, AI integration, microservices, cloud deployments, and engineering practice maturity. Mentor engineers, collaborate with product managers, improve resilience and scalability, and replace manual operations with intelligent network automation.

Serve as a trusted executive partner to an AI R&D organization, driving strategy, stakeholder alignment, program delivery, financial and labor planning, organizational change, and associate engagement while enabling operational excellence.

Lead product management for enterprise product and experience initiatives, partnering with design, data, ML, and engineering to define strategy, prioritize roadmap, prototype solutions, and deliver customer-centric, technology-driven products that achieve business outcomes.

Lead enterprise product and experience initiatives, guiding cross-functional teams (product, data, design, ML, engineering) to define strategy, deliver customer-centric digital products, prototype and apply emerging AI/automation tools, and drive transformational outcomes across Capital One.

Drive strategy, roadmap, and continuous improvement of Card Servicing Intelligence products using GenAI and NLP. Own feature decomposition, acceptance criteria, and MVP delivery while partnering with data science, engineering, design, and stakeholders to deploy automated servicing solutions and measure business impact.

Lead and execute compliance assessments of scripts and models (CoDIR), identify high-risk scripts, prepare and report findings, monitor governance inventory, and explore GenAI/automation to improve processes.

Build and validate statistical and machine learning models for credit card loss forecasting, modernization, and allowance (CCAR). Partner with data scientists, engineers, and product managers to transform data, automate insights, support model governance, and present model risk impacts to executives using cloud and big-data tools.

Lead development of machine learning and NLP solutions for operational risk analytics: partner with cross-functional teams, build end-to-end models (design, training, evaluation, validation, implementation), analyze large numeric and text datasets, deliver insights and reporting to senior leaders, and experiment with emerging generative AI techniques to improve risk management.

The Distinguished Engineer will drive innovation in banking technology, lead software engineering projects, mentor teams, and utilize AI and advanced architectures for risk management.

The Senior Associate, Data Scientist will partner with cross-functional teams to identify risks associated with models and validate machine learning models across the enterprise, enhancing decision-making while ensuring model governance and risk management.

Lead and grow a full-stack engineering team delivering cloud-native, microservices-based risk management solutions. Collaborate with product managers, mentor engineers, oversee architecture and delivery, and occasionally contribute code. Drive adoption of modern cloud, container, and AI/ML technologies to build scalable, compliant systems.

Lead and deliver advanced analytics and machine learning solutions to support pricing, revenue optimization, and partner relationships. Collaborate with cross-functional teams to design, validate, and implement models at scale using large transaction datasets and modern big-data tooling. Translate technical insights into actionable business recommendations.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

Lead applied research on agentic AI for cybersecurity: set scientific direction, run experiments on efficient post-trained models, design evaluation/verification methodologies, partner with product and security teams, and translate research into secure, production-ready agentic systems.

Lead AI strategy, partner engagements, and operational execution across CrowdStrike products. Align R&D and product delivery to company AI strategy, prioritize initiatives, set organizational goals, drive thought leadership, and create order in complex cybersecurity AI environments while delivering customer value.

Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Support process-based risk assessments across Enterprise Services, identify and mitigate process-level risks, advise stakeholders, research emerging regulatory and technology risks, draft deliverables, manage multiple projects and workstreams, and drive process and controls improvements to strengthen the company's risk posture.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Lead AMP's multidisciplinary engineering organization to define and execute technology vision and roadmap. Responsible for scaling teams across hardware, software, AI, and infrastructure; driving product development, quality systems, and reliable deployments; partnering with execs and cross-functional teams on budgets, prioritization, and metrics; recruiting and mentoring leaders; and balancing short-term delivery with long-term investments to create business value.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.

Lead long-term technical strategy and execution across multiple teams, architecting and evolving foundational frontend/backend systems and platforms at massive scale. Drive AI/ML adoption, solve ambiguous, high-impact problems, remain hands-on in critical system design and implementation, and align senior leaders on roadmaps and engineering standards.