Top Tech Jobs & Startup Jobs

Design, develop, and maintain autonomous driving platform and middleware. Integrate vehicle sensors, implement sensor abstraction layers, build data recording/mining and service frameworks, optimize performance and data serialization, improve startup UX, and collaborate with hardware, algorithms, cloud, safety, and platform teams to deliver scalable, high-performance system software for AV fleets.

Analyze, profile, and optimize AI training workloads on NVIDIA GPUs across the hardware/software stack. Implement production-quality software, build MLPerf training submissions, model workloads in simulators, and develop automation tools to identify and resolve performance bottlenecks.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Lead cross-stack adoption of modern toolchains, build and code-health practices, and performance workflows on NVIDIA CPU platforms. Integrate and validate compilers, linkers, cross-compilation, and CI; profile and optimize large codebases; build automation and playbooks; drive integration blockers to owners and communicate measurable before-and-after results to engineering teams and leadership.

Lead alignment of NVIDIAcompiler codebases with open-source projects, build tooling and workflows to improve developer productivity, automate rebasing/patch management and apply AI-assisted workflows, while partnering with architecture and CI teams to scale changes across NVIDIA.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Architect, develop, and debug system software for NVIDIA Halos across QNX, Linux, and hypervisor environments. Build BSP, boot, drivers, I/O virtualization, and platform software for NVIDIA SoCs. Ensure safety and security compliance (ISO 26262/21434), use formal methods, optimize platform performance, and collaborate with ASIC, firmware, OS, safety, and security teams. Employ AI-assisted engineering tools to accelerate development, testing, and documentation.

Lead a small team building a new Linux kernel-mode GPU driver: design architecture, implement device/kernel-mode code, collaborate with and upstream patches to the Linux kernel community, and represent NVIDIA in open-source discussions.

Lead verification for GPU/SoC display IP: define verification scope and testplans, build UVM-based infrastructure, develop UVM and C/C++ testbenches, drive coverage closure, debug silicon integrations, and collaborate with architects, partners, and customers globally.

Lead production readiness and process optimization for NVIDIA AI datacenter platforms. Define test specs, validate DFx and signal integrity, qualify components, collaborate with NPI and contract manufacturers, analyze volume data to improve yield, and support customer/quality escalations to meet cost, throughput, and DPPM targets.