Top Machine Learning Jobs in Richmond, VA

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Develop and validate machine learning models, quantify model risks, challenge production models, and contribute to enterprise model governance. Analyze large-scale numeric and textual datasets using Python, AWS, Spark, and related tools. Partner with data scientists, engineers, and product managers, validate models across business domains, and communicate model-risk implications to executives.

Owns ML infrastructure and model delivery product roadmaps for Capital One’s Card Intelligence team. Defines customer-focused problems, prioritizes capabilities, establishes success metrics, partners with data science and technology teams, leads agile ceremonies, and drives scalable solutions aligned with business outcomes. Requires product management experience or a quantitative, business, or marketing degree, with preferred experience in data engineering, MLOps, machine learning infrastructure, or software engineering.

Conduct model risk audits using econometric, statistical, and machine learning methods. Review enterprise models and risk practices, identify issues, develop analytical solutions, communicate findings to varied audiences, and maintain transparent documentation. Partner cross-functionally with business lines and build cloud-based, data-driven solutions. Required expertise includes regression, survival analysis, panel data, time-series analysis, machine learning, large datasets, and programming in R, Python, or SQL.

Leads data science initiatives for Capital One’s Card Mainstreet business. Partners with data scientists, software engineers, and product managers to develop customer-focused solutions; analyzes large-scale numeric and textual data; builds, validates, and implements machine learning models; translates technical findings into business outcomes; and supports innovation and team talent development.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Serve as a technical leader supporting sales across enterprise, service provider, and public sector customers. Evaluate F5 solutions, explain technical and business trade-offs, conduct demonstrations and presentations, and collaborate with Sales, Marketing, Product Management, and Development. Provide expertise in application delivery, security, cloud, Kubernetes, containers, Linux, NGINX, APIs, AI infrastructure, and automation. Act as a technical evangelist, publish educational content, mentor solution engineering teams, and support strategic opportunities and product launches.

Leads technology strategy, delivery, and operations for the Claims organization. Owns the Guidewire ClaimCenter platform, integrations, cloud adoption, application support, AI and automation initiatives, governance, budgets, vendors, and regulatory alignment. Partners with Claims executives to prioritize technology portfolios and deliver measurable improvements in claim outcomes, adjuster productivity, cycle time, and expenses. Manages multi-team engineering, configuration, analysis, QA, and offshore delivery organizations across the US and UK.

Leads and develops a data science team applying statistics, machine learning, causal inference, and AI to business problems. Establishes standards for experimentation, model governance, validation, responsible AI, and reproducibility. Partners with business, engineering, BI, and IT stakeholders to prioritize analytics initiatives, translate business questions into solutions, deploy models into production, and communicate actionable insights to executives and nontechnical audiences. Oversees career development, stakeholder adoption, and strategic alignment.

Leads the technical direction and execution of a machine learning team focused on personalization, relevance, ranking, search, and advertising. Designs, scales, and operates production ML systems, including training, evaluation, deployment, and serving infrastructure. Partners with product, data, and engineering stakeholders, drives architecture and system design decisions, mentors engineers, and uses AI tools to accelerate development and explore LLM-powered features.

Leads Propel America's product and technology strategy, including the Fellow Portal, AI integrations, technology roadmap, software development, Salesforce, infrastructure, data systems, cybersecurity, and vendor relationships. Manages technical staff and outsourced IT providers, oversees architecture, QA, releases, compliance, data privacy, and disaster recovery, and partners cross-functionally to translate organizational needs into scalable products and technology solutions.

Own Fetch’s on-platform advertising measurement strategy, including event taxonomies, attribution, incrementality, reporting, and data pipeline standardization. Lead migration to unified measurement standards, partner with engineering and data science on scalable infrastructure and methodologies, and develop third-party measurement partnerships. Serve as the internal measurement expert for stakeholders, advertisers, and external partners.

Leads enterprise-wide technical and transformation initiatives across Product, Technology, Operations, Data, Security, and Innovation. Responsibilities include program governance, requirements gathering, product documentation, architecture and process modeling, delivery coordination, risk and dependency management, AI use-case evaluation, vendor assessment, and executive stakeholder communication. The role establishes documentation standards and supports modernization through Agile practices, integrations, automation, and responsible AI governance.

Own the market growth strategy for Luma in biologics research, including segmentation, positioning, competitive analysis, sales enablement, AI use-case development, thought leadership, and customer adoption. Partner across Siemens, Product, Sales, Marketing, Finance, and Services to translate biologics research needs into software value, build market credibility, support strategic pursuits, and drive pipeline and wins.

Designs and builds data ingestion, feature engineering, analytics, and machine learning pipelines using healthcare and other structured or unstructured data. Develops, tunes, deploys, monitors, and documents predictive models and enterprise data products. Collaborates with data scientists, engineers, product managers, and stakeholders while ensuring data quality, reliability, and production engineering standards.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Builds and maintains Power BI reports, dashboards, data models, DAX calculations, and row-level security. Writes SQL queries for reporting, analysis, and data validation; investigates warehouse structures and inconsistencies; and partners with stakeholders and data teams to deliver accurate insights. The role manages recurring and ad hoc reporting requests, communicates findings through data visualization, and supports business decisions across sales, marketing, and operations.

Teaches undergraduate and graduate artificial intelligence courses in an online and blended format. Develops syllabi, lesson plans, course materials, assessments, and student feedback aligned with university objectives. Uses online technologies and course software to facilitate learning, integrates responsible AI practices, and evaluates appropriate student use of AI tools. Requires a doctorate, applied AI experience, AI-enabled software development expertise, strong communication skills, and commitment to ongoing faculty development.

Leads enterprise AI architecture strategy, standards, governance, and roadmaps across products, platforms, data, and business processes. Guides solutions involving generative AI, agentic AI, RAG, machine learning, automation, cloud platforms, and AI infrastructure. Partners with executives and technical teams on architecture reviews, security, compliance, scalability, and cost optimization; mentors senior engineers and provides hands-on leadership through prototypes and critical problem resolution.

Leads commercial analytics and business intelligence strategy, including executive dashboards, account reporting, revenue and pricing analytics, operational KPIs, forecasting, segmentation, and predictive modeling. Partners with Sales, Finance, Marketing, Operations, and Product to improve revenue, profitability, customer retention, and marketplace performance. Presents insights to executives and customers while managing analytics teams, budgets, vendors, and BI scalability.

Senior Commercial Counsel will draft, review, and negotiate complex subscription, partnership, vendor, and other commercial agreements. The role primarily supports a global Sales organization, advises cross-functional teams, manages contracts managers, and handles enterprise and strategic partnership transactions. Responsibilities include balancing competing priorities, supporting legal projects, and providing practical, business-minded legal guidance in a fast-paced environment.