Top Tech Jobs & Startup Jobs in Seattle, WA

Develop and optimize high-performance math kernels for NVIDIA GPUs using CUTLASS, focusing on Tensor Core-based deep learning kernels in CUDA C++ and Python.

Responsible for analyzing architectural requirements, delivering high-performance RTL blocks, collaborating with engineers, and assisting in IP solution integration.

Lead performance and scalability characterization of the DGX Cloud stack across Kubernetes and NVIDIA components. Design automated workload tests, build monitoring and CI/CD frameworks, triage large-scale Kubernetes issues, collaborate with researchers and upstream open-source communities, and present findings externally.

Lead cross-disciplinary software architecture for next-generation CPUs and SoCs, collaborating with hardware, software, ASIC, verification, and platform teams. Influence hardware decisions via deep system software, OS/driver, and performance expertise, advise partners and senior management, and drive optimized solutions for AI and HPC workloads.

The role involves developing AI models for fault-tolerant quantum systems, conducting research in quantum computing, and collaborating with teams to integrate AI solutions into quantum hardware.

Design and build real-time, physics-informed AI models and synthetic data pipelines for quantum calibration and decoding. Develop surrogate models and GPU-accelerated implementations, apply reinforcement and online learning for calibration policies, co-design low-latency calibration-decoding systems, and collaborate across engineering and research to enable fault-tolerant quantum hardware.

Lead development and maintenance of node provisioning, OS image build/packaging, and nodepool lifecycle for NVIDIA Kubernetes Engine. Build CAPI providers, BYON onboarding, hardened image pipelines with automated CVE remediation and compliance gating, and automated test suites. Troubleshoot node-layer failures at scale, collaborate with upstream Kubernetes/CAPI communities, and ensure reliable GPU-optimized node operations across large clusters.

Work on performance and scalability of Kubernetes-based accelerated runtimes for AI, from orchestration to hardware. Design upstream Kubernetes changes, improve container startup and encrypted container performance, build large-scale simulation and CI-based tests, collaborate with researchers and upstream communities, and present results publicly.

Design and scale production infrastructure for LLM-powered agents and copilots. Build reliable, high-performance services, APIs, CI/CD, and integrations; collaborate with research and hardware teams; mentor peers and drive continuous innovation in agentic AI applications.

The role involves analyzing deep learning networks, optimizing compiler algorithms, and collaborating across teams to enhance deep learning software performance and usability.