AI Engineer

• De sign, de velop, and fine-tune a variety of AI models
• Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration
• Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
• Implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
• Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning
• Maintain model registries, monitoring, and version control of context protocols
• Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
• Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
• Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI-safety techniques; translate new research into operational capabilities

AI Engineer – Mission Innovation Lab

• Define and evolve the standards, architecture patterns, and tooling choices that govern how engineers use AI across the codebase — including where to invest in codebase readiness (instrumentation, specification, evaluation) versus targeted rewrites.
• Own execution of the multi-milestone plan (readiness → validated → governed) applied to corgi and secondary repos, in partnership with squad leads — without being their manager.
• Decide what AI tooling, agent infrastructure, and evaluation pipelines the org needs, and build or oversee the build of the ones that don't exist yet.
• Set the policy and technical controls for where human-in-the-loop review is required versus where autonomous agent action (PR review, deployment) is safe — resolving the tension between adoption speed and security/quality risk.
• Write the guidance engineers actually use — AGENTS.md/CLAUDE.md conventions, review norms for AI-generated code, eval standards — and make sure it's adopted, not just published.

AI Architect

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal Engineer

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Senior Lead AI Engineer – SDKs, Gen AI Evaluation, MCP

• Partner with AI Solution Engineers, Architects, and senior technical teammates
• Design, build, and deploy AI-powered solutions for legal customers
• Translate legal workflows and business needs into technical solutions
• Contribute to technical discovery, solution delivery, and prototyping
• Assist with demonstrations, proofs of concept, and early-stage prototypes
• Develop working knowledge of legal terminology and processes
• Design and implement AI-powered solutions for legal workflows
• Collaborate with senior engineers and architects to ensure solutions are scalable
• Evaluate LLM outputs for legal accuracy and relevance

Software Engineer – AI, CoCounsel Forward Deployed Engineering

Machine Learning Engineer

• Design, develop, and deploy production machine learning models supporting Torc's perception systems.
• Own end-to-end model development for scoped perception problem areas, from data preparation and training through evaluation and deployment.
• Write production-quality ML code to support scalable training, evaluation, and inference workflows.
• Analyze model performance, identify failure modes, and iterate to improve robustness, accuracy, and generalization across diverse driving environments.
• Develop and optimize perception models leveraging multi-modal sensor data, with an emphasis on camera-based and 3D perception systems.
• Collaborate with software engineers, infrastructure teams, and autonomy engineers to integrate perception models into production autonomy software.
• Support improvements to training pipelines, data workflows, and tooling that accelerate model iteration and deployment.
• Contribute to model architecture discussions and technical decision-making within the team.
• Mentor junior engineers on implementation, experimentation, and machine learning best practices.

Senior Machine Learning Engineer – 3D Perception

• Design, develop, train, evaluate, and deploy machine learning models supporting mission objectives.
• Collaborate with Government stakeholders to define AI/ML problems, success metrics, and evaluation criteria.
• Develop Model Selection Briefs documenting model performance, trade studies, reproducibility artifacts, and technical recommendations.
• Integrate Government-provided human baseline results into model evaluation and performance analysis.
• Design and coordinate Human-in-the-Loop (HITL) baseline experiments when baseline data does not exist.
• Support implementation and operational integration of production-ready machine learning models.
• Collaborate with data engineers to prepare, validate, and optimize datasets for AI/ML workloads.
• Develop backend services and APIs supporting AI-enabled applications.
• Support cloud-native model development, deployment, and lifecycle management within secure environments.
• Participate in architecture discussions, code reviews, testing, documentation, and quality assurance activities.
• Work closely with Government customers and Agile development teams to deliver innovative AI solutions that meet mission requirements.

Machine Learning Engineer, AI Model Developer – Mid to Senior, Top Secret Clearance Required

Machine Learning Engineer – AI Model Developer, Mid to Senior

• Be a hands-on leader driving the development of next-generation AI solutions for the future of shopping.
• Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community.
• Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment.
• Utilize programming languages like JavaScript, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services

Senior Lead Software Engineer, Full Stack – AI

• Guide your team and collaborate with cross-functional stakeholders with a focus on four key areas: Human Centered, Business Focused, Technology Driven, and Transformational Leadership
• Define clear and actionable problem statements to help teams deliver results
• Own and prioritize three-year ML infrastructure and model delivery roadmaps that tie to business outcomes
• Understand and leverage technology to drive comprehensive design decisions on white space technical problems
• Develop and communicate a 3-year vision to senior stakeholders and partner teams
• Attract and recruit top talent, and be intentional about mentoring and developing junior product associates

Senior Manager, Product Management – Machine Learning and AI

• Collaborate with talented data scientists and engineers to develop and deploy machine learning solutions to help support our customers
• Work across teams to build products that enable seamless and scalable model development
• Influence strategic decisions and contribute to building a thriving product practice in support of our overall Machine Learning capabilities
• Collaborate with tech teams to build comprehensive acceptance criteria
• Breakdown business epics into features and further into user stories
• Demonstrate a growth mindset using hypothesis-driven assessment of data
• Own and prioritize feature development roadmaps
• Work autonomously in an agile environment to conduct research and develop features
• Collaborate with designers, technologists, data scientists and subject-matter experts

Senior Associate Product Management – Machine Learning, AI

• Support the development and operationalization of AI and agentic AI systems in production mission environments
• Collaborate with multidisciplinary teams of analysts, engineers, and developers to solve complex challenges
• Deliver innovative, production-ready algorithms and platforms
• Apply cutting-edge techniques in statistical analysis, predictive analytics, entity resolution, graph analytics, explainable AI, and operational analytics
• Contribute to the integration of AI, DevSecOps, data engineering, platform engineering, cybersecurity, and observability in a fast-paced, engineering-centric environment

• Build our agentic SDLC pipeline. Design, implement, and operate agentic workflows across the development lifecycle - planning, code generation, review, testing, documentation, and release that is all integrated with our GitOps and CI/CD infrastructure.
• Level up developer experience. Bring modern AI integrations into the daily workflow of our engineering team: coding agents, automated review, intelligent test generation, and self-service tooling that removes friction and multiplies output.
• Engineer grounded, trustworthy systems. Build retrieval-grounded and context-aware AI systems that produce reliable, verifiable results and design the evaluation harnesses that prove it.
• Establish governance and guardrails. Define and implement the policies, human-in-the-loop checkpoints, audit trails, and security controls that let us adopt agentic AI responsibly in environments where trust is non-negotiable.
• Formalize and scale. Turn what works into repeatable frameworks, playbooks, and internal platforms that extend agentic acceleration beyond engineering to the whole organization.
• Champion the practice. Evangelize, teach, and support AI adoption across the team; you'll be a force multiplier, not a silo.

AI Workflow Engineer

• Writes, tests and refines prompts across Tax, Assurance, Advisory and Wealth Management use cases, following the architectural standards and prompt patterns established by the Workflow Architect.
• Builds, maintains and versions Claude skills and workflow components, managing the full lifecycle from development through testing to production deployment.
• Curates and organizes the Firm’s prompt library, tagging entries by service line, use case type, complexity tier and validation status to maximize discoverability and reuse across teams.
• Conducts quality assurance testing on AI outputs, including accuracy validation, edge case testing, hallucination detection and compliance checks against the Firm’s professional standards.
• Supports the Ambassador network by providing hands-on technical assistance, troubleshooting workflow issues and delivering implementation guidance when new tools and skills launch.
• Documents prompt engineering patterns, skill specifications and build processes in the CoE knowledge base, creating the reference material that enables the team to scale.
• Collaborates with service line professionals and subject matter advisors to capture domain-specific requirements and translate them into prompts and skills that reflect how work actually gets done.
• Participates in use case intake, evaluating submitted ideas from across the Firm for technical feasibility, effort estimate and alignment with the existing production roadmap.
• Monitors AI platform updates and new capabilities (e.g., Anthropic Claude releases, new tool-use and agentic features) and assesses their impact on existing skills and workflows.
• Contributes to the Firm’s AI training materials by developing prompt examples, live skill demonstrations and technical reference guides that the Coordination Track uses for Ambassador enablement.

AI Prompt Engineer

• Develop and implement AI-driven solutions that enhance and scale AI adoption across Navy Federal Credit Union.
• Lead the design and implementation of cutting-edge AI systems using large language models to solve complex business problems.
• Collaborate with ETS and Business partners to build and drive AI solutions.
• Provide delivery and ongoing support for data science, advanced analytics, and augmented intelligence technologies.
• Exhibit excellent problem-solving skills and demonstrate strong leadership qualities with a proven track record of delivering measurable outcomes.

Principal AI Engineer – Agentic AI

• Build complete applications, workflow automations, and basic agents within your team's product area using AI coding agents
• Translate customer needs into buildable specifications and ship solutions from requirements through deployment
• Instrument solutions to track usage and outcomes, iterating based on what you learn
• Evaluate AI-generated output against Remitly's standards for security, privacy, reliability, and quality before shipping
• Participate in customer conversations to understand acute problems and identify automation opportunities
• Document your approaches, effective prompts, and patterns for the team

Senior AI Native Software Engineer

AI Native Software Engineer II

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

• Evaluate emerging advancements across hyperscalers, leading AI labs, academia, and venture-backed companies.
• Conduct hands-on technical assessments, benchmarks, and proof-of-concepts to validate applicability and performance.
• Own the technical delivery of end-to-end AI systems spanning infrastructure, frontier LLMs, agentic and multi-agent applications and physical AI pipelines.
• Work with business units, to support hands-on integration of emerging AI technologies in complex customer environments such as cloud, hybrid, edge, or disconnected.
• Provide day-to-day technical leadership to engineers and researchers.
• Conduct design reviews, guide architectural decisions, and raise the overall technical bar of the Applied Research team.
• Convert prototype learnings into reusable frameworks, reference architectures, accelerators, and technical artifacts that scale across programs and business units.

Machine Learning Engineer jobs in Virginia

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AI Engineer – Mission Innovation Lab

AI Architect

AI Legal Engineer

Senior Lead AI Engineer – SDKs, Gen AI Evaluation, MCP

Software Engineer – AI, CoCounsel Forward Deployed Engineering

Senior Machine Learning Engineer – 3D Perception

Machine Learning Engineer, AI Model Developer – Mid to Senior, Top Secret Clearance Required

Machine Learning Engineer – AI Model Developer, Mid to Senior

Senior Lead Software Engineer, Full Stack – AI

Senior Manager, Product Management – Machine Learning and AI

Senior Associate Product Management – Machine Learning, AI

AI Engineer

AI Workflow Engineer

AI Prompt Engineer

Principal AI Engineer – Agentic AI

Senior AI Native Software Engineer

AI Native Software Engineer II

Senior Software Engineer – Applied AI

Senior Software Engineer – Applied AI

Lead AI Research Engineer