Top AI Engineer Jobs in Seattle, WA

Lead design and delivery of reliable, scalable AI/ML platform services. Build production-grade code, reusable APIs/SDKs, observability and resilience tooling, define reliability standards, support on-call incident resolution, and mentor engineers to enable secure, compliant generative AI use cases.

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Design, prototype, and deploy LLM-powered agents, RAG workflows, and automations that accelerate internal teams and customer-facing product features. Integrate AI systems with SaaS tools, build reusable components and evaluation frameworks, embed with business teams to deliver measurable outcomes, and establish standards for responsible AI, monitoring, and cost control across the company.

Design and implement multi-step agentic AI workflows (orchestration, retrieval, tool use, state management) using frameworks like LangGraph/LangChain. Build retrieval pipelines over proprietary CRE data, evaluate and monitor model quality with human-in-the-loop processes, integrate Anthropic models on AWS Bedrock, implement guardrails and observability, and rapidly prototype features with Product and Design to deliver trustworthy, source-linked AI capabilities that perform real business actions.

Design, build, and operate production AI and data systems for marketing: LLM/agent orchestration, retrieval pipelines, data pipelines, CDP and warehouse architecture, automation of manual workflows, data quality and reliability, stakeholder collaboration, and mentoring. Ship production-grade Python and SQL solutions and evaluate/monitor AI systems.

Own the technical vision and architecture for an AI platform of agents, skills, and tools. Scale retrieval/knowledge systems, build developer experience, orchestration, observability, and evaluation/feedback loops. Align cross-division engineering strategy and drive platform adoption.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, including LLM inference infrastructure, RAG, AI agents, and evaluation/observability. Design and run experiments (A/B tests), optimize systems for production performance and reliability, and iterate rapidly with product and design partners.

Design and build AI agents, first-party web and mobile apps, and scalable backend services. Integrate LLM frameworks and enterprise systems, implement APIs and microservices, ensure observability and security, and collaborate with Finance, Security, and Engineering stakeholders.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

This role involves defining AI architecture, improving system quality, leading teams, and building production-ready AI systems at Drata.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

Design and implement scalable platform services, runtimes, and developer tooling for deploying and managing AI workloads on Arm-based edge devices. Build abstractions, observability, and performance optimizations across heterogeneous hardware while collaborating with architects and AI engineers to produce robust, maintainable systems and SDKs for on-device AI.

Develop and run rigorous benchmarks and profiles for LLM inference workloads to identify bottlenecks and drive targeted performance optimizations across frameworks, runtimes, and GPU hardware. Design experiments (quantization, caching, speculative decoding), validate changes against real traces, partner with platform engineers to productionize improvements, and produce clear technical writeups to guide model-serving, runtime, and hardware decisions.

Lead technical direction and architecture for DAP Repository Flows, design and ship autonomous and scheduled agents that operate on customer repositories, improve DAP onboarding, collaborate across teams on platform interfaces, mentor engineers, and solve high-scale technical problems to raise engineering quality.

As a Staff AI Engineer, you'll lead AI product development, overseeing technical direction, system deployment, and model evaluation while collaborating across teams.

The role involves designing AI systems for mental healthcare, focusing on building scalable, production-grade systems that enhance patient engagement and clinical operations. Responsibilities include setting technical standards and ensuring safety and reliability while working with advanced AI technologies.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Architect and lead the enterprise AI platform roadmap, building scalable developer environments, multi-modal AI pipelines, agent orchestration, vector-data solutions, RAG/cognitive search capabilities, vendor ecosystems, and guardrails for model observability, drift detection, security, and responsible AI. Collaborate cross-functionally to accelerate adoption and embed AI across the enterprise.

Build and evaluate autonomous and semi-autonomous agent workflows that interact with browsers, codebases, and APIs. Design modular tool/memory/evaluator frameworks, develop data pipelines for SFT and RL training, benchmark and prompt strategies, and partner with stakeholders to align AI behavior with user needs.

Act as a technical partner to internal teams to design and implement LLM-powered applications. Build prototypes and proofs-of-concept, advise on system-of-system architecture, support end-to-end deployment, and keep teams current on LLM techniques, safety, and best practices while coordinating cross-functional stakeholders.

Design, build, and operate large-scale AI/HPC infrastructure spanning job scheduling (Beaker), runtimes, networking, and storage. Drive automation, performance optimization, root-cause analysis for distributed GPU workloads, mentor engineers, and collaborate with researchers to prioritize and deliver scalable orchestration and cluster health tooling.