Top Tech Jobs & Startup Jobs in Seattle, WA

Leads market research and intelligence for CoreWeave’s B2B AI cloud business. Responsibilities include sizing TAM and SAM, tracking competitive share, conducting brand and audience research, identifying influential buyer personas, developing market and account signals, and delivering dashboards and insights to marketing and sales. The role partners with Brand, Product Marketing, and Engineering to improve targeting, competitive positioning, go-to-market strategy, and adoption of market intelligence across campaign teams.

Leads systems engineering teams developing high-throughput file, block, and object storage for AI cloud infrastructure. Drives architecture, performance tuning, latency optimization, benchmarking, and 10x scaling across distributed storage systems. Provides technical mentorship, performance management, recruitment, and cross-functional leadership while maintaining a coding-focused culture and maximizing GPU utilization.

Lead and develop an infrastructure engineering team responsible for internal Kubernetes platforms and foundational services. Set roadmaps, ownership, operating practices, and success metrics; guide reliable infrastructure and automation; establish testing, observability, SLO, incident response, change management, and on-call practices; resolve cross-team dependencies; and manage hiring, coaching, performance, priorities, and risk communication.

Manage technical revenue accounting and ensure ASC 606 and US GAAP compliance. Review sales contracts, develop accounting policies, prepare technical position papers, support SOX controls and audits, monitor accounting guidance, improve systems and automation, and provide cross-functional training. The role partners with Sales, Legal, FP&A, Product Management, Deal Desk, Business Systems, and General Ledger Accounting teams while supporting accurate revenue recognition and financial reporting.

Design, build, and operate secure, high-performance sandboxed runtime environments for multi-tenant Kubernetes workloads. Develop GPU-aware scheduling, isolation, and resource management strategies; integrate container runtimes, lightweight virtual machines, and virtualization technologies; optimize container, VM, I/O, and GPU performance; conduct profiling and benchmarking; and contribute to Linux, runtime, virtualization, GPU, and security architecture.

Own fleet-wide reliability and operations programs for a rapidly scaling AI cloud infrastructure platform. Establish reliability metrics, dashboards, OKRs, and success criteria; identify systemic gaps across compute, storage, networking, hardware, firmware, software, and operations; and lead cross-functional initiatives that improve fleet readiness, availability, utilization, and stability. Manage dependencies, risks, incident follow-ups, root cause actions, and executive communications while ensuring improvements scale with fleet growth.

Build and operate CoreWeave’s multi-cloud internal infrastructure across GCP, Azure, and AWS. Manage cloud foundations, IAM, networking, Terraform infrastructure as code, monitoring, logging, CI/CD, backups, disaster recovery, and production reliability. Troubleshoot complex infrastructure and application issues, support cloud migrations and acquisitions, implement secure architecture patterns, and contribute to incident response and root-cause analysis. Mentor junior engineers and communicate technical decisions across teams.

Design, build, and operate secure sandboxed runtime environments for Kubernetes and GPU-accelerated workloads. Develop GPU-aware scheduling, isolation, and resource management strategies; optimize container, VM, I/O, and GPU performance; conduct profiling and benchmarking; and contribute to Linux, container runtime, virtualization, and GPU driver architecture. Collaborate with security, platform, and infrastructure teams to establish runtime isolation and performance standards for multi-tenant systems.

Own the W&B Models product roadmap for ML experiment tracking, training visualization, monitoring, evaluations, and large-scale analysis. Partner with researchers, engineering, design, and go-to-market teams to understand workflows, define product strategy, ship polished experiences, and improve performance using customer feedback and usage data. The role requires strong ML research fluency, technical product management experience, data visualization instincts, AI tool proficiency, and clear stakeholder communication.

Partner with Product and Engineering leaders to make data-driven people decisions that support growth and execution. Own workforce planning, talent calibration, performance management, job architecture, organizational change, and complex people matters. Identify workforce patterns and risks, provide actionable insights, coach leaders, and collaborate with People Team centers of excellence to deliver scalable solutions in a fast-growing environment.