Top AI & Machine Learning Jobs in Seattle, WA

Lead and grow the engineering team productizing and operating an inference offering. Drive roadmap execution with Product, improve reliability, observability, release and incident processes, integrate platform capabilities, and deliver developer-friendly tooling and application-layer enhancements to scale production ML workloads.

Lead go-to-market and technical strategy for AI developer tooling, winning early customers and building repeatable playbooks. Translate customer MLOps requirements into product feedback, partner with engineering and Weights & Biases, and enable sales/solutions teams to close deals around experiment tracking, model governance, and LLM observability.

Lead cross-functional programs to validate and optimize AI/ML infrastructure performance. Drive benchmarking, observability, hardware bring-up, release readiness, and measurable metrics across GPU-based clusters. Coordinate engineering, infrastructure, product, capacity, and go-to-market teams to operationalize benchmarking frameworks, prioritize performance work, and ensure platforms meet stability and performance standards for training and inference workloads.

Lead cross-functional programs for inference platform delivery, customer onboarding, launch readiness, and runtime optimization. Drive roadmap outcomes for latency, throughput, uptime, and price-performance. Build metrics, dashboards, launch gates, and repeatable processes for release validation, performance regression tracking, and postmortems. Align Engineering, Product, Infrastructure, and GTM teams to deliver reliable, scalable inference services and operational excellence.

Join a small Applied Training team building Kubernetes-native research cluster and sandbox infrastructure for large-scale ML training. Design and implement cluster experiences (CLI, job schemas, operators), own Python SDKs for RL rollouts, work directly with customers and infra teams, document OSS training frameworks, and ship production-grade ML infrastructure solving distribution, scheduling, and isolation at scale.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Senior Software Engineer focused on AI infrastructure performance insights and observability. Responsible for designing and building monitoring, instrumentation, metrics, and tooling to measure and optimize compute and platform performance while collaborating with infrastructure and ML teams.

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

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.