AI Engineer

• 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

LLM Engineer

• Build and integrate crucial public good infrastructure to support evidence-based AI-native data system implementation that strengthens data feedback loops.
• Lead a research and development portfolio to test, co-develop, and refine integrated data and AI infrastructure solutions in real-world education and workforce contexts.
• Design and lead iterative learning cycles at the intersection of the AI Infrastructure and System Feedback Loops (SFL) portfolios.
• Generate actionable evidence about what conditions, infrastructure configurations, and integrated approaches accelerate impact for learners.
• Apply mastery-level expertise in implementation research and education data systems, and a proactive, solutions-oriented perspective to navigate ambiguity and drive thorough, equity-centered inquiry in partnership with grantees, researchers, and cross-divisional colleagues.

Senior Program Officer, Data & AI Infrastructure Integration

• 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.

Lead AI Research Engineer

• Build full-stack AI applications using React, JavaScript/TypeScript, and Python
• Develop both rapid prototypes and production-ready applications
• Integrate AI capabilities using OpenAI APIs and related AI services
• Design and implement frontend interfaces, backend APIs, database interactions, and application workflows
• Deploy and manage applications using tools such as Vercel and AWS
• Work with Supabase for backend services such as authentication, database management, storage, and APIs
• Use AI-assisted development tools such as Cursor, v0, Codex, and Claude Code to accelerate development
• Debug, test, document, and improve applications for performance, reliability, and usability
• Collaborate with product, strategy, design, and technical teammates to move ideas from concept to shipped product

AI Full Stack Developer

• Design, build, and deploy AI-powered applications including chatbots, knowledge assistants, and workflow automation agents.
• Implement end-to-end solutions covering data ingestion, transformation, prompt orchestration, model interaction, and cloud deployment.
• Integrate AI systems with internal APIs, enterprise platforms, and data pipelines.
• Design agent workflows with tool/function calling, branching logic, retries, and fallback handling.
• Implement human-in-the-loop and approval-based workflows for regulated financial use cases.
• Build multi-agent systems for validation, refinement, and complex task decomposition.
• Design and implement RAG pipelines covering chunking, embeddings, retrieval, and grounding.
• Work with structured and unstructured data using SQL, S3, and data pipeline tools.
• Leverage AWS services (S3, Glue, Redshift, Lambda, ECS, Step Functions, SQS/SNS) for storage, transformation, and orchestration.
• Monitor and improve AI systems for accuracy, latency, cost, and reliability.
• Implement structured output validation, schema enforcement, and guardrails.
• Evaluate model performance and iteratively improve grounding and output consistency.

AI Engineer – Financial Services

• The Lead, Network Engineer designs, analyzes, plans and modifies network components supporting customer communication implementation activities.
• Work on problems of diverse scope and complexity ranging from moderate to substantial.
• Be the technical SME for our enterprise WAN, remote LAN/WLAN, and data center SD-WAN network services.
• Responsible for engineering, implementing, and operating a multi-vendor SD-WAN environment, scalable campus/branch switching and wireless, and resilient remote branch solutions.
• Serve as the escalation point for complex routing, overlay, and authentication issues, including RADIUS/TACACS+.
• Provide technical leadership to a team of network engineers.
• Work closely with the Lead Network Architect and Principal Network Architect through structured technical solution peer reviews.

Lead Network Engineer – AI

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Work closely with business stakeholders to rapidly develop, demonstrate, and refine AI solutions.
• Take a hands-on engineering approach focused on building rather than advising.
• Work directly with the client to translate business problems into AI-enabled prototypes and production-ready solutions.

• Monitor and analyze emerging AI policy, regulatory, and legislative developments across the United States and key international markets
• Develop policy positions, briefing materials, white papers, and executive-level recommendations on issues related to artificial intelligence, national security, technology competitiveness, workforce transformation, and digital infrastructure
• Build and maintain relationships with policymakers, government agencies, think tanks, industry associations, and academic institutions
• Support executive engagement with Congress, the Executive Branch, regulators, and international government stakeholders
• Represent Cognition in policy discussions, industry working groups, conferences, and strategic forums
• Partner with technical teams to translate complex AI capabilities into clear, actionable policy narratives
• Identify opportunities for public-private partnerships that accelerate AI adoption across government and critical industries
• Help anticipate policy risks and opportunities that could impact company strategy, product development, or market expansion

AI Policy Lead

• Contribute to the design of AI-enabled solution architectures within the Avanade Catalyst Solutions portfolio
• Apply and help develop reusable patterns, reference architectures, and accelerators
• Adapt pre-built AI solutions to support client needs and evolving market requirements
• Ensure solutions meet standards for scalability, security, and production readiness
• Collaborate with engineering and delivery teams to identify opportunities for AI-driven tools and methods
• Support the development of repeatable, outcome-based services
• Help drive consistency in how AI solutions are implemented across engagements
• Support architecture and deployment of ready-made AI offerings
• Partner with security, risk, and compliance teams to align with enterprise requirements
• Provide guidance to delivery teams

Manager, Global AI Enterprise Architect

• Design and build GenAI use cases based on client's needs and specific data
• Understanding of Agentic AI frameworks and design and when they are appropriate for use
• Develop and deploy generative AI models using platforms such as Amazon Kendra, Google Vertex AI, and AWS Bedrock.
• Implement large language models (LLMs) and transformer models to create advanced AI applications.
• Integrate solutions with cloud-based AI services such as Google Cloud Document AI, Google Cloud Gemini, Google Cloud Gemma, and Google Cloud Generative AI Language Models.
• Show your curiosity, motivation, and passion for experimentation and continuous learning to drive AI innovation that is applicable across enterprise client domains.

GenAI Engineer

• Own two of the most strategically important product surfaces in our platform: AI-Native Intelligence Dashboard and Payments & Monetization
• Lead the team of senior engineers, set technical direction, and ship AI-powered capabilities that customers can see, feel, and measure
• Multiply your impact by leading, mentoring, and developing Engineering Managers and senior engineers
• Own the outcome for execution across multiple AI engineering teams
• Drive an AI First approach to engineering
• Practice Change Agility by guiding teams through evolving AI technologies
• Apply First Principles Problem Solving to complex AI and product challenges
• Leverage Data-Driven Decision Making to assess system performance
• Collaborate cross-functionally with Product, Design, Data, Security Research, and other Engineering leaders

Senior Manager, AI Engineering

Machine Learning Engineer

• Debug and troubleshoot complex deployment and integration challenges across customer environments • Work directly with customer engineering and platform teams to deploy and operationalize AI systems • Design and implement integrations across enterprise AI workflows, APIs, infrastructure, and governance platforms • Deploy and support AI evaluation frameworks, observability solutions, and guardrail implementations • Translate customer deployment challenges into actionable feedback for product and engineering teams • Partner with engineering, infrastructure, security, risk, compliance, and operations stakeholders to navigate enterprise governance requirements • Drive successful customer adoption of AI infrastructure solutions while balancing technical and business requirements

AI/ML Engineer

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches
• Collaborate cross-functionally with product/business stakeholders to translate workflows into well-scoped deliverables

• Manage mission-critical collaboration and content management systems
• Integrate AI capabilities such as intelligent search, workflow automation, and context-aware assistance
• Collaborate closely with stakeholders, system owners, security teams, and end users
• Stay current with Department AI initiatives and systems
• Design, develop, test, implement, and deploy SharePoint infrastructure and solution components
• Drive innovation within the M365 and SharePoint environments
• Conduct stakeholder consultations and workshops

M365 Automation and AI Developer

• Build and ship production-grade AI systems used daily by real customers
• Design multimodal pipelines for drawings, specs, and construction documents
• Build agentic workflows automating preconstruction tasks: Document intake, Extraction, Takeoff support, Bid preparation, QA workflows
• Build and improve: Retrieval systems (embeddings, reranking, chunking), Evaluation systems, Orchestration layers
• Optimize model quality, latency, reliability, and cost efficiency
• Prototype and iterate quickly on customer-facing AI features
• Own systems end-to-end (experiment → deploy → monitor → improve)
• Help shape engineering culture and long-term technical direction

Founding AI/ML Engineer

• Collaborate with a cross-functional group of technologists, product managers, and archivists to shape the future of ethical AI in journalism.
• Use generative AI to surface archival content for contemporary use.
• Partner with RAD to design and prototype workflows that transform unstructured archival data into structured, discoverable assets.
• Establish standards for the ethical use of artificial intelligence with NPR's archival content.
• Prepare digital collections for machine learning and research approaches that pair AI with human editorial judgment and metadata to find new insights from the archive.
• Identify opportunities and build functional prototypes that enable reporters and producers to query historical audio for contemporary reporting.
• Assist in the development of automated workflows to transform unstructured legacy media into a searchable, AI-indexed library.
• Analyze historical data troves to surface evergreen content opportunities and extract narrative leads that support contemporary reporting.
• Evaluate AI outputs and discovery tools for accuracy and bias to ensure technical prototypes maintain archival integrity.

AI Research Intern, Ethics and Readiness

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS
• Engineer robust runtime behavior
• Enable workflow automation and integration
• Create and maintain technical documentation
• Collaborate cross-functionally with product/business stakeholders
• Other duties as assigned

Machine Learning Engineer jobs in District of Columbia

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AI Architect

AI Legal Engineer

Senior Program Officer, Data & AI Infrastructure Integration

Lead AI Research Engineer

AI Full Stack Developer

AI Engineer – Financial Services

Lead Network Engineer – AI

AI & Automations Lead

AI Engineer

AI Policy Lead

Manager, Global AI Enterprise Architect

GenAI Engineer

Senior Manager, AI Engineering

AI/ML Engineer

AI Research Scientist, Applied AI

AI Engineer

M365 Automation and AI Developer

Founding AI/ML Engineer

AI Research Intern, Ethics and Readiness

AI Engineer