Top Senior Machine Learning Jobs in Richmond, VA

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining modeling strategy, designing experiments, analyzing telemetry, influencing senior leaders, validating and stress-testing models, and overseeing the full model lifecycle from design through implementation. The role partners with engineering and research teams and requires extensive experience in large-scale analytics, machine learning, relational databases, cloud computing, and open-source programming.

Leads financial statement audits and evaluates governance, risk management, regulatory compliance, and internal controls for digital asset and financial services clients. Directs complex audit engagements, applies advanced auditing and technical accounting expertise, identifies material misstatements, and issues auditor reports. The role also involves client relationship management, coaching and developing diverse teams, communicating findings, and maintaining professional, independence, and ethical standards.

Leads large-scale technical programs for AI and ML platforms, coordinating researchers, engineers, business leaders, and cross-functional teams from concept through production. The role manages technical risks, delivery commitments, stakeholder alignment, and execution across distributed systems and real-time data platforms. It also establishes and grows the technical program management discipline, mentors TPMs, and guides senior leadership decisions in a highly regulated banking environment.

Leads personal loan acquisition data science, developing and deploying machine learning models for customer targeting, campaign optimization, underwriting, pricing, and verification. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using Python, Spark, AWS, and related technologies; validates and backtests models; translates technical findings into business outcomes; and develops team talent.

Build and deploy machine learning models for personal loan acquisition, including underwriting, pricing, verification, list selection, and campaign optimization. Analyze large-scale numeric and textual datasets using Python, Spark, AWS, H2O, and related tools. Collaborate with data scientists, engineers, and product managers, validate and backtest models, and translate statistical insights into business outcomes.

Leads data science strategy for Consumer and Developer Experience, partnering with engineering, analytics, and product teams. Builds and deploys machine learning models across design, training, validation, evaluation, and implementation. Analyzes large-scale numeric and textual datasets using Python, cloud platforms, and open-source technologies, translating complex findings into business decisions and customer-focused digital products.

Leads data scientists, business analysts, and data analysts in developing machine learning models from design through implementation. Partners with software engineering and product teams to deliver customer-focused products, using Python, cloud computing, Spark, H2O, and other technologies to analyze large-scale numeric and textual data. Translates complex technical findings into business goals while developing team talent and evaluating emerging methods.

Leads data science for foundational AI and automation across a global payments network. Partners with data scientists, engineers, ML engineers, and product managers to develop scalable models and AI agents using PyTorch, AWS, vector databases, and LLMs. Serves as an NLP and LLM domain expert, translates technical work into business value, develops talent, and delivers production-scale libraries, platforms, and solutions.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

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 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.

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.

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.

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.

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.

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.

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.

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.