Top Tech Jobs & Startup Jobs in Seattle, WA

The Senior Production Engineer will lead the engineering of reliable systems for cloud operations, focusing on architecture, automation, and incident response.

Design and implement solutions for managing a global server fleet, utilizing Go and Python, while working with Kubernetes and automation tools.

As a Staff Product Manager, you'll lead the strategy and execution for CoreWeave's data services, engaging with customers and overseeing product development in a fast-paced environment.

Lead and grow an infrastructure support team to maintain and optimize physical data center and GPU-based compute environments. Own incident and escalation management, improve support processes, manage ticket workflows and 24/7 shift logistics, and collaborate with product and engineering to ensure reliable hardware delivery and client communication.

Support data center construction cost planning, forecasting, and controls across project lifecycles. Maintain program-level cost forecasts, prepare estimates and commercial risk assessments, compile benchmarking data, and coordinate with engineering and external cost consultants to optimize capital efficiency and inform location and deployment decisions.

Lead end-to-end delivery of hyperscale data center builds and capacity expansions. Translate capacity forecasts into technical program requirements, manage cross-functional execution with Engineering, Supply Chain, and Operations, mitigate schedule and budget risks, report KPIs to leadership, and establish scalable processes and SLAs for global build teams.

Lead go-to-market for CoreWeave's data services by identifying storage and data-pipeline opportunities, engaging enterprise buyers, shaping product roadmap through field feedback, and designing commercial/technical playbooks for multi-petabyte deals to drive GPU adoption and storage-heavy closes.

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.

Own and operate a Kubernetes-based, multi-region data platform to ensure high availability and performance. Build and optimize CI/CD pipelines, observability, IaC, and security for data-intensive, distributed systems. Drive incident response, capacity planning, and reliability engineering for production-grade services.