Top Tech Jobs & Startup Jobs in Seattle, WA

Design and implement CUDA Tile compiler transformations, MLIR dialects, lowering passes, and optimization techniques for NVIDIA GPUs. Optimize tile-based kernel performance across GPU generations, define public APIs, conduct performance analysis, and develop robust software, tests, and debugging workflows. The role requires independent project ownership and collaboration within a product-focused engineering team.

Develop and deploy scalable AI solutions, distributed training and inference systems, and MLOps pipelines across cloud and NVIDIA platforms. Serve as a technical lead for customers and partners, optimize large-scale workloads for performance and cost, diagnose full-stack infrastructure problems, and create open-source tools and reference architectures. The role requires strong AI/ML lifecycle experience, systems expertise, programming skills, and communication abilities.

Lead product strategy and multi-quarter roadmaps for NVIDIA’s AI Factory software-defined networking stack. Guide upstream development across OVN, OVS, and OVN-Kubernetes; define requirements, priorities, and architecture trade-offs; coordinate dependencies across DPU, fabric, virtualization, and engineering teams; and translate partner and operator needs into product releases, reference deployments, and community strategy.

Conduct fundamental and applied robotics research across perception, planning, control, and learning. Develop algorithms for manipulation and loco-manipulation, integrate methods into real robotic systems and simulators, publish and engage with academia, transfer research to products, collaborate across teams, and mentor interns.

Lead performance and scalability engineering for Kubernetes-based accelerated AI runtimes. Analyze end-to-end stack from orchestration to hardware, design upstream Kubernetes changes, improve container startup and encrypted inference performance, build large-scale simulations and automated workload tests, and engage upstream communities and customers to validate and ship scalable AI infrastructure.

Develop and improve NVIDIA's proprietary GPU compiler backend in C++. Design and implement compiler passes and optimizations, collaborate with hardware and application teams, and support graphics and compute toolchains for high-performance GPU instruction generation.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

Design, develop, test, and optimize system software for next-generation ARM SoCs in pre- and post-silicon phases. Analyze system-wide performance bottlenecks, recommend HW/SW policies, perform competitive workload comparisons, and drive full-stack adoption of performance optimizations across NVIDIA software and OSS SDKs.

Lead implementation of AI infrastructure solutions on NCP/Neo Cloud and DGX Cloud. Deploy and optimize distributed training/inference, manage Kubernetes/GPU scheduling, profile and tune large-scale workloads, implement reference architectures and integrations, build runbooks and monitoring, and provide remote and on-site technical support to strategic customers.