Top Tech Jobs & Startup Jobs in Seattle, WA

Design and operationalize firmware and software architectures for next-generation NVIDIA Edge AI platforms. Define scalable platform requirements with hardware, software, ISV, and IHV partners; develop reliable, debuggable firmware, diagnostics, and deployment tools; contribute from pre-silicon design through production; make technical decisions amid ambiguity; and mentor architects and engineering teams.

Develop and enhance network hardware and software profiling tools using C/C++ and Python. Collaborate with customers, architects, hardware and software engineers, and operations teams to define requirements, design solutions, and deliver tools for optimizing complex GPU, CPU, and networking systems across HPC, cloud, AI, embedded, and automotive environments.

Leads end-to-end execution of complex software platform programs across cloud infrastructure, platform services, system integration, validation, release delivery, and operational support. Coordinates software, infrastructure, product, and operations teams; manages dependencies, risks, technical issues, schedules, metrics, and senior-leadership reporting. Ensures full-stack readiness and delivery of scalable, reliable software supporting AI workloads.

Leads NVIDIA Cloud Partner Day 2 operational readiness for large-scale GPU infrastructure. Builds continuous validation, observability, automated detection and remediation, fleet lifecycle management, runbooks, SLOs, and reusable operational frameworks. Partners with engineering and operations teams to improve reliability, performance, and readiness across compute, networking, storage, Kubernetes, and AI workloads.

Serve as a trusted technical advisor to NVIDIA Cloud Partners to design, integrate, and deploy large-scale GPU AI/HPC cloud infrastructure. Lead PoCs and production deployments of models, frameworks, and software stacks, run technical workshops and presentations, collaborate with sales, engineering, and product teams, and drive customer adoption and consumption.

Lead performance characterization and optimization of large-scale AI workloads across GPUs, networking, storage, and software. Design and run benchmarks and profiling, diagnose bottlenecks, define evaluation methodologies and metrics, produce actionable optimization plans, and collaborate with DL engineers, platform teams, and GPU architects to deliver improvements.

Lead development of local AI agent frameworks and runtimes on Windows and GeForce RTX GPUs. Optimize agent inference, sandboxing, and privacy/security policies, collaborate with AI research and driver teams, mentor engineers, and deliver production-ready, local autonomous agent systems.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Develop silicon-accurate GPU kernel microbenchmarks, model-level performance analysis, and agentic kernel optimization to maximize LLM inference throughput and latency. Attribute bottlenecks across kernel, compiler, and runtime, produce optimization policies, and collaborate with compiler, hardware, kernel, and framework teams to deliver production-grade performance improvements.

Develop and optimize Vulkan graphics drivers to maximize performance, power-efficiency, and latency for games and creative apps. Diagnose GPU/CPU bottlenecks, implement driver performance improvements, fix defects, collaborate across teams, and communicate API usage changes to third-party developers.