Top Tech Jobs & Startup Jobs in Seattle, WA

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.

Lead development and optimization of local AI agent frameworks and runtimes for Windows PCs with NVIDIA GeForce RTX GPUs. Build secure, sandboxed, privacy-focused agent execution and inference systems, collaborate with AI research, driver, and open-source teams, and establish deployment guidelines. Mentor engineers while delivering production-ready code using Windows systems programming, C++, Python, LLM inference pipelines, GPU acceleration, and agent orchestration frameworks.

Lead security engineering for RTOS, hypervisor, and embedded virtualization: design and review defenses, collaborate on CPU/SoC security, perform threat modeling and automotive TARA, review low-level C/C++ code, build fuzzing and test infrastructure, assess vulnerabilities, and drive security fixes and verification toward automotive safety and security standards.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.

Develop and support NVIDIA networking technologies for AI clusters, including InfiniBand, NVLink, and Spectrum-X. Reproduce and root-cause complex customer issues down to source code, collaborate with R&D on fixes, analyze high-performance network metrics, and create diagnostic tools. The role requires extensive C and Python development in Linux and embedded systems, networking expertise, customer support experience, and strong communication skills. Occasional weekend or holiday support may be required.

Lead an engineering team responsible for reliability processes and platforms, including incident management, maintenance, on-call, issue management, and production readiness. Own the roadmap, technical direction, delivery, adoption, and continuous improvement of operational systems. Partner across infrastructure, product, and security teams; lead major incident response; hire and develop engineers; and apply automation, AI, and operational insights to improve service reliability and reduce manual work.

Verify ASIC inference accelerator designs at block and SoC levels using advanced methodologies, random stimulus, functional coverage, assertions, and UVM. Develop verification infrastructure, debug designs, optimize automated flows, and collaborate with architects, designers, and pre- and post-silicon teams. Apply knowledge of ASIC design flow, power verification, DFT, netlist verification, and machine learning techniques to improve verification processes.

Architect and develop low-level GPU power and performance-management software, including P-state management, DVFS, real-time controllers, and power-management policies. Lead features through architecture, validation, silicon bring-up, productization, and deployment. Collaborate across hardware, firmware, driver, validation, and data-center teams while debugging complex system issues and developing automation for performance, power efficiency, stability, and reliability.

Design and optimize future GPU and processor memory systems for power, performance, area, and cost efficiency. Develop architecture and microarchitecture features, prototype models, and simulation workflows. Analyze benchmarks and performance or power results, debug issues across models, RTL, silicon, and systems, and collaborate with external partners. The role requires deep CPU/GPU architecture and memory systems expertise, power analysis experience, and proficiency in C, C++, and scripting languages.

Develop production-grade multimodal AI systems for L3/L4 autonomous vehicles. Build sensor-fusion and spatiotemporal world models combining camera, LiDAR, radar, navigation, and vehicle-state data. Create multitask models for road understanding, occupancy, perception, prediction, and planning; develop scalable training and evaluation pipelines; investigate foundation-model approaches; optimize models for real-time NVIDIA hardware; and collaborate across AV engineering teams to deploy robust systems.