Top Tech Jobs & Startup Jobs in Seattle, WA

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

Design and optimize high-speed, high-density ASIC package layouts and substrate breakouts. Perform pinout optimization, routing, placement, stack-up, power distribution, CAD library development, feasibility studies, and productivity methodology improvements while collaborating with package leads and cross-functional teams.

Design and perform physical layout for silicon interposers using Cadence Virtuoso; run DRC/LVS and verification (Calibre, ICV, Thunder); perform feasibility studies, floorplanning, custom layout, and collaborate with package, SI, and technology teams to meet schedule, cost, manufacturability, and electrical goals.

Design and evaluate routing policies for LLM traffic, build and run agentic benchmarks, produce calibration data and routing profiles, contribute code and docs to open-source, and collaborate across engineering teams to integrate optimized inference software into NVIDIA's serving stack.

Lead and scale an engineering team building GPU-accelerated deep learning inference frameworks. Drive strategy and roadmap, partner with compiler/libraries/research teams, oversee performance tuning and multi-GPU optimizations, mentor engineers, and deliver production-ready inference pipelines for LLM, multimodal, and generative AI across datacenter and edge platforms.

Develop and optimize GPU-accelerated algorithms for quantum circuit simulations, collaborating with various teams, and leading technical efforts.

Design, implement, and optimize scalable high-performance dense linear algebra kernels for GPUs. Provide technical leadership, collaborate with product and partners, improve performance and quality, and contribute to library roadmaps and development practices.

The role involves performing data analysis for datacenter applications, using machine learning for GPU systems, and providing insights on trends to enhance workflows.

Profile, benchmark, and optimize large-scale LLM training and inference across GPUs, CPUs, HCAs, and switches. Focus on collective communication and high-performance networking, build PyTorch trace-based profiling and replay tools, define performance test plans, and collaborate across hardware and software teams to identify bottlenecks and achieve performance targets.

Engage industry enterprise developers and AI startups to accelerate adoption of NVIDIA GPU-accelerated platforms. Provide technical guidance, create developer-facing content (samples, tutorials, blogs, trainings), collaborate with internal SDK and research teams, troubleshoot implementation issues, and contribute to hands-on training and DLI courses as an instructor.