AI Engineer

• Define and maintain enterprise architecture standards, reference patterns, and governance guardrails for AI-enabled solutions moving from pilot to production.
• Establish decision models for build, buy, or integrate choices; model and vendor selection; data access; security; and responsible AI adoption.
• Design scalable architecture patterns for AI-enabled workflows and agentic processes across data, APIs, identity, security, and cloud platforms.
• Test new AI models, LLMs, agent frameworks, and tools to inform enterprise adoption.
• Design proof-of-concepts, define success criteria, and guide solutions from pilot to production.
• Lead architecture reviews and communicate trade-offs, risks, and growth to partners.
• Partner with teams to translate needs into governed, implementation-ready solutions.
• Ensure teams through demos, workshops, and reusable templates to support safe AI adoption.

Principal Architect – Enterprise AI

• As a Senior AI Engineer within Bristol Myers Squibb's AI Venture Studio team, you will be a hands-on individual contributor responsible for building secure cloud-hosted applications.
• Design APIs, services, infrastructure patterns, deployment pipelines, and semantic-layer evolution patterns for agent context engineering.
• Collaborate with frontend engineers to define clean API contracts, streaming response patterns, error handling, and service-level behaviors for AI-powered user experiences.
• Create and maintain CI/CD pipelines, environment configuration, automated tests, and release processes for cloud AI applications.
• Partner with AI Engineers, Data Engineers, Data Scientists, and product teams to solve complex delivery challenges.
• Continuously refine delivery priorities and deliver MVP transition activities by maturing AI capabilities.

Senior AI Engineer

Machine Learning Engineer

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
• Scale performance of DL models across different architectures and types of NVIDIA accelerators.
• Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
• Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.

Deep Learning Software Engineer, Inference

LLM Engineer

• Collaborate closely with our AI and ML research teams to understand their infrastructure needs and obstacles
• Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization
• Help define and improve important measures of AI researcher efficiency, ensuring that our actions are in line with measurable results
• Collaborate with diverse teams, including researchers, data engineers, and DevOps professionals, to build a seamless and coordinated AI/ML infrastructure ecosystem
• Stay on top of the latest advancements in AI/ML technologies, frameworks, and effective strategies, and promote their implementation within the company

AI and ML Infra Software Engineer, GPU Clusters

• 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

• Improve operational efficiency, resilience, and process quality by establishing scalable AI automation architecture standards and governance guardrails
• Accelerate transformation delivery and reduce rework through clear design frameworks, decision models, and reusable automation patterns
• Enhance employee and customer experience by reducing manual work, process fragmentation, and automation risk
• Embed AI-first architectural practices that improve automation scale, decision quality, and measurable business outcomes
• Lead evaluation of AI automation, agentic workflow, and transformation initiatives across the organization
• Define architectural decision frameworks and long-term platform strategy for n8n, Boomi, APIs, event-driven orchestration, and AI services to reduce duplication and increase reuse
• Establish governance guardrails for AI-enabled workflows, agents, prompts, models, data flows, and human-in-the-loop controls to ensure scalability, reliability, auditability, and responsible use
• Anticipate growth, compliance, security, and operational risks across automation platforms and transformation programs before they materialize
• Influence cross-functional roadmaps with AI Automation Engineers, BSAs, and business leaders based on architectural trade-offs, platform fit, and measurable outcomes
• Translate enterprise capability needs into clear, implementable technical designs that accelerate AI automation delivery
• Create architectural blueprints for intelligent workflows, agentic process patterns, and cross-system orchestration that reduce ambiguity and minimize rework
• Design secure, scalable, and resilient automation patterns across SaaS systems, internal platforms, APIs, data ecosystems, and AI/ML services
• Drive API-first, event-driven, and human-in-the-loop architecture standards that improve interoperability, control, and long-term maintainability
• Lead architecture reviews and represent AI automation and transformation strategy in executive and cross-functional forums

Principal Enterprise AI Architect

AI Research Scientist

• Collaborate with research mentors to formulate research projects or novel applications of machine learning aligned with the team's mission, with a focus on AI applied to medicine, finance, or law
• Conduct independent and collaborative research and publish high-quality work at top AI and domain-applied research venues
• Design and execute large-scale experiments using modern deep learning frameworks, writing high-quality, reusable code
• Develop models and systems that bridge AI capabilities with real-world application requirements in high-stakes, regulated domains
• Engage across teams — including with domain experts and applied engineering — to ground research in practical constraints and real-world impact
• Contribute to the broader research community through publications, open-source releases, and collaboration with academic and industry partners

Post-Doctoral AI Researcher – 2 year Fixed-Term

• Partner with internal and external stakeholders to re-envision and scale Core Engineering technologies, including the design and build of a suite of AI Agents
• Drive technical excellence and governance of AI Agents as use-cases are identified across Snap Engineering
• Collaborate across teams to understand product requirements, evaluate architectural trade-offs, and deliver solutions needed to build innovative products and services
• Champion best practices in availability, scalability, operational excellence, and cost management
• Serve as a technical mentor to senior engineers and a thought partner to engineering leaders
• Contribute to the technical roadmap for AI Agents and Core SWE infrastructure, ensuring alignment with organizational goals
• Partner closely with Product, Finance, Sales, and Engineering teams distributed across multiple offices
• Influence key architectural and implementation decisions for scalable, reliable, and cost-effective engineering solutions
• Iterate quickly without compromising quality and continuously raise the bar on operational excellence
• Adapt technical plans nimbly in response to evolving customer and business needs

Staff Software Engineer, DevProd AI

• Design & Implement ML Solutions: Take ownership of the end-to-end ML lifecycle for your projects, from ideation and research to deployment and monitoring.
• Test, Learn, and Iterate: Design and analyze tests to validate your models and quantify their business impact and design future iterations.
• Collaborate and Communicate: Partner closely with product managers, engineers, and business stakeholders to understand requirements, define problems, and communicate your findings and results effectively.

Machine Learning Scientist II

• Design and implement end-to-end model pipelines to production across multiple product domains, including ranking, recommendations, search, and personalization
• Develop and maintain scalable data pipelines, data quality checks, and model monitoring to ensure reliability, performance, and responsible behavior of ML systems in production
• Collaborate with cross-functional partners (product, analytics, engineering) to translate ambiguous business needs into well-scoped ML projects, communicate findings, and influence decision making with data-driven insights
• Use A/B tests and offline/online evaluation frameworks to measure model impact and guide iterative improvement

Machine Learning Scientist III – Whole Trip AI

• 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 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

• Serve features reliably for large-scale batch inference and low-latency online inference
• Maintain features in a centralized, versioned feature registry
• Design and manage feature storage solutions for correctness, freshness and efficient retrieval
• Build scalable, distributed systems with attention to compute and storage efficiency
• Enforce engineering quality through validation, testing, monitoring, and observability

Staff Software Engineer, ML Feature Store

• Design, develop, and improve scalable infrastructure to support the next generation of AI applications
• Drive improvements in architecture, performance, and reliability
• Collaborate across hardware, software, and research teams
• Stay informed of the latest advancements in AI infrastructure

Senior Applied AI Engineer

• Lead the end-to-end system design, architectural framework, and product integration of Workday’s next generation of intelligent agents.
• Architect how foundational models are safely and reliably integrated into functional, production-grade software.
• Own the design, experimentation, and orchestration of complex agentic workflows, translating cutting-edge AI capabilities into enterprise-grade business value.
• Be a core champion for Responsible and Governed AI—architecting systems with strict guardrails for data privacy, predictability, and explainability.
• Balance high-level system design and hands-on execution, solving critical product constraints like latency, cost, and reliability.

Principal AI 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

• 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 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

Machine Learning Engineer jobs in Washington

See all jobs on JobTailor

Principal Architect – Enterprise AI

Senior AI Engineer

Deep Learning Software Engineer, Inference

AI and ML Infra Software Engineer, GPU Clusters

AI Architect

Principal Enterprise AI Architect

Post-Doctoral AI Researcher – 2 year Fixed-Term

Staff Software Engineer, DevProd AI

Machine Learning Scientist II

Staff Software Engineer, DevProd AI

Machine Learning Scientist III – Whole Trip AI

AI Legal Engineer

Software Engineer – AI, CoCounsel Forward Deployed Engineering

Staff Software Engineer, ML Feature Store

Senior Applied AI Engineer

Principal AI Engineer

Senior Program Officer, Data & AI Infrastructure Integration

Senior AI Native Software Engineer

AI Native Software Engineer II

Senior Software Engineer – Applied AI