Top AI & Machine Learning Jobs in Seattle, WA

Lead applied research to advance continuous learning for agents: design and evaluate LLM post‑training and RL methods, implement and deploy experiments at scale, validate research on customer tasks, optimize GPU/distributed training, and mentor engineers while driving cross‑functional technical direction.

Drive applied research to enable continuous learning for agents: design and evaluate LLM post-training methods (fine-tuning, RL, distillation), run large-scale GPU experiments, validate approaches on customer tasks, and help deploy and scale models and infrastructure across the stack.

Author, profile, and optimize CUDA GPU kernels for LLM inference to maximize throughput and minimize latency. Build reproducible microbenchmarks and MLPerf workflows, collaborate across product, hardware, and orchestration teams, lead designs and reviews, mentor engineers, and translate kernel wins into end-to-end model-serving performance improvements.

Design, implement, and maintain infrastructure and tools to validate GPU performance at scale. Develop performance tests, automation workflows, and Kubernetes controllers/operators, extend open-source tooling for metrics and observability, troubleshoot production systems, and participate in on-call rotation.

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 production-grade full-stack, AI-enabled applications. Develop React/Next.js frontends, backend services on Kubernetes, integrate LLM/AI features, connect data platforms, implement CI/CD, automated testing, observability, and ensure secure, high-performance APIs and services.