Top Senior Machine Learning Jobs in Richmond, VA

Lead Data Engineer developing cloud-based data and AI solutions for risk management. Responsibilities include building, testing, deploying, and supporting full-stack systems; developing data pipelines, distributed and streaming applications; using cloud warehouses, RDBMS, NoSQL databases, and machine learning technologies; conducting code reviews and unit tests; collaborating across Agile teams; and mentoring engineers. The role requires application development, big data, and cloud experience, with preferred expertise in real-time data, data warehousing, distributed computing, and AI tooling.

Leads a team of software developers and a portfolio of technology projects focused on risk management and regulatory solutions. Builds and deploys cloud-based, AI-powered full-stack applications using distributed microservices and modern databases. Collaborates with product managers and cross-functional partners, mentors engineers, evaluates code, drives technology adoption, and oversees robust solutions using AWS and related technologies.

Lead a data science team developing scalable AI and NLP solutions for customer-facing financial applications. Partner with data scientists, engineers, product managers, and designers to build, train, evaluate, fine-tune, and operationalize machine learning and large language models. Drive research into generative AI, training optimization, self-supervised learning, explainability, and RLHF while delivering production systems at scale. Translate technical findings into business outcomes and develop team talent.

Develop the strategic direction for Capital One’s AI and machine learning platforms. Identify customer and business outcomes, define reusable platform capabilities, build strategy materials, solve cross-functional business problems, and support continuous delivery and improvement of AIML products. The role requires quantitative and qualitative analysis, stakeholder influence, senior leadership communication, people management, and project management.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

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.

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.

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.

Design, build, deploy, and support scalable Java microservices on AWS. Provide technical leadership, architectural direction, code reviews, troubleshooting, proof-of-concept development, and cross-functional collaboration. Work with high-throughput data, messaging systems, distributed architectures, observability, and AI-assisted development tools. Participate in on-call support, mentor engineers, lead architectural initiatives, and deliver reliable SaaS platform features.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

Own the strategic vision and roadmap for Coursera’s search experience, including conversational, personalized, semantic, multilingual, and AI-driven discovery. Partner with engineering, design, research, data science, marketing, and product teams to improve search accuracy, conversion, and learner engagement. Lead experimentation, KPI measurement, competitive analysis, and revenue-focused personalization initiatives across a global learning platform.

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.

Leads a software engineering portfolio and people-management team building cloud-based, full-stack risk technology solutions. Oversees distributed microservices, regulatory technology, AI-powered risk management tools, and collaboration with product managers. Mentors engineers, evaluates code, promotes modern technology practices, and may contribute hands-on development using multiple programming languages, databases, containers, Kubernetes, and cloud services.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

Provides strategic legal counsel to cybersecurity and enterprise data teams, advising on incident response, technical risk, data use, privacy, emerging technology, and governance. Collaborates with compliance, risk, technical, and legal stakeholders to manage regulatory and reputational risks. Monitors cyber and data legislation, supports disclosures and regulatory filings, and advises on AI and product development matters.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Leads process risk assessments and mitigation initiatives across Enterprise Services, including cyber, cloud, network, data, resiliency, product, and third-party functions. Advises business partners, researches emerging risks and regulatory changes, manages multiple projects and workstreams, facilitates stakeholder interactions, reports outcomes to senior leadership, and drives process and control improvements.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Lead multiple product-focused engineering teams to design, build, deploy, and modernize risk-management software using full-stack technologies, microservices, AWS, Big Data, and ML; mentor engineers, improve engineering practices, and drive migration from monoliths to scalable services.