Top Machine Learning Jobs in Richmond, VA

Leads technology risk assessments and mitigation initiatives across enterprise services, including cybersecurity, cloud, infrastructure, data, resilience, and third-party functions. Advises stakeholders on risk management frameworks, regulatory changes, controls, process improvements, and emerging risks. Manages multiple projects, facilitates assessments, communicates outcomes to senior leadership, and drives operational efficiency and risk reduction.

Leads endpoint detection engineering using AI, machine learning, Detection-as-Code, behavioral analytics, and MITRE ATT&CK. Owns detection coverage from telemetry onboarding through alert deployment, tuning, and gap analysis. Partners with threat intelligence, security operations, and business stakeholders to manage cyber risk and compliance. Conducts threat research, aligns detection capabilities with red-team techniques, mentors engineers, and drives architecture decisions for scalable, high-fidelity endpoint protections.

Leads strategic HR partnership for Capital One’s Applied AI and technology organizations. Responsibilities include organizational design, talent and workforce planning, succession planning, recruiting and retention strategy, executive coaching, job architecture, human capital risk mitigation, and scaling an AI organization from 800 to 1,200 employees. The role partners with senior leaders and HR functions to develop talent strategies, manage scarce AI/ML talent, support enterprise HR initiatives, and drive organizational effectiveness.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Leads enterprise data science initiatives for agentic AI products, including AI/ML model development, experimentation, telemetry analysis, and strategic recommendations for senior leadership. Partners with data scientists, engineers, and product managers while translating complex statistical insights into business outcomes. Requires extensive experience in quantitative analytics, large-scale data analysis, production AI/ML deployment, A/B testing, and team leadership.

Own and scale Jellyfish’s security engineering program, including strategy, architecture, secure SDLC governance, AI security, threat modeling, vulnerability remediation, incident response, penetration testing, and bug bounty programs. The role combines hands-on software development with executive advising, cross-functional leadership, mentorship, and security risk management across SaaS infrastructure and customer-facing AI products.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Designs, develops, tests, validates, and deploys AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Builds data and model-training pipelines, leverages cloud platforms, evaluates model performance, documents methodologies, collaborates with technical teams, and guides prototype-to-production transitions. Requires five years of relevant experience and an active TS/SCI clearance with polygraph.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

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.

Leads software engineering teams and technology portfolios while designing and implementing large-scale, distributed, cloud-native systems. Responsibilities include architectural leadership, full-stack development, platform roadmap strategy, mentoring engineers, regulatory solutions, and delivery of highly reliable services. The role uses Java, Python, Go, AWS, Kubernetes, databases, machine learning systems, and observability practices to support real-time fraud detection and financial transaction protection.

Staff Full-stack Engineer responsible for defining technical vision, designing scalable cloud solutions, modernizing monoliths into microservices and event-driven platforms, and establishing architectural and API standards. The role provides hands-on technical leadership, mentors engineers, leads cross-domain initiatives, and advises on performance, scalability, operability, and data architecture. Candidates need extensive software engineering, cloud, distributed systems, database, networking, and architecture experience, along with expertise applying AI tools to improve development efficiency.

Design, build, deploy, and support scalable cloud data platforms, pipelines, and applications for risk management. Lead technical direction across data engineering, analytics, and machine learning teams; establish reusable engineering patterns; implement security and privacy controls; and collaborate with product managers and software engineers. The role includes mentoring, stakeholder communication, distributed data processing, data modeling, warehousing, streaming, and both relational and NoSQL database solutions.

Leads engineering teams and portfolios delivering large-scale, cloud-native risk management systems. Responsibilities include designing distributed microservices and full-stack solutions, guiding architecture and technology strategy, mentoring engineers, collaborating with product and architecture leaders, and building AI-powered regulatory and risk tools. The role uses multiple programming languages, cloud platforms, databases, containers, APIs, observability, CI/CD, and infrastructure-as-code practices.

Leads diverse technology projects and developer teams building cloud-based, full-stack risk management solutions. Provides technical direction across Agile teams, defines architecture and service integrations, mentors engineers, establishes engineering standards, and drives system performance and technical debt reduction. Develops distributed microservices and APIs using modern programming languages, databases, containers, AWS services, AI tools, CI/CD, and observability practices.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

Lead full-stack engineering projects and developers building cloud-based, AI-powered risk management solutions. Design distributed microservices, APIs, databases, and user-facing systems using modern programming languages and AWS technologies. Provide technical leadership across Agile teams, establish engineering standards, mentor engineers, improve system performance, reduce technical debt, and influence long-term technology strategy while supporting regulatory requirements.

Lead data science for US Card customer management by partnering with data scientists, engineers, product managers, and business teams. Build, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical modeling, clustering, classification, sentiment analysis, time series, and deep learning to generate customer insights and forecasts. Translate technical findings into business outcomes while contributing to innovation, stakeholder collaboration, and talent development.

Lead engineering projects and developers building cloud-based, AI-powered risk management solutions. Design and deploy full-stack microservices, APIs, and distributed systems using modern programming languages, databases, AWS services, containers, and messaging technologies. Provide technical leadership across Agile teams, establish engineering standards, mentor engineers, partner with architects and product managers, reduce technical debt, improve system performance, and influence long-term technology strategy.

Leads engineering projects and developers building cloud-based, AI-powered risk management solutions. Designs distributed microservices and full-stack systems, establishes technical standards, partners with architects and product managers, improves system performance, reduces technical debt, and mentors engineers. The role uses AWS technologies, modern programming languages, databases, APIs, CI/CD, infrastructure as code, automated testing, and observability within Agile teams.

Build, deploy, scale, and monitor production machine learning models and infrastructure for risk management. Responsibilities include model development, data pipeline construction, cloud and distributed systems operation, Kubernetes-based deployment, CI/CD, model governance, responsible AI, testing, monitoring, and collaboration with product and data science teams.

Build AI-driven governance and resilience solutions for enterprise cloud infrastructure. Design intelligent guardrails, distributed systems, microservices, and full-stack platforms using Go, Python, Java, TypeScript, AWS, Docker, and Kubernetes. Lead architectural vision, technical roadmaps, testing, deployment, and optimization while writing code, conducting reviews, mentoring engineers, and collaborating with product, data science, and risk teams.