Top Tech Jobs & Startup Jobs

Architect and develop diagnostic systems for NVIDIA data center platforms across hardware and software interfaces. Lead platform bring-up, hardware validation, failure root-cause analysis, and diagnostic deployment across ODM factories, cloud providers, and partner production lines. Establish diagnostic architecture and product roadmaps, develop tools using C, C++, and Python, collaborate with multidisciplinary teams, and mentor engineering staff.

Lead system verification and emulation for GPUs, SoCs, switches, and NICs. Bring up and debug high- and low-speed protocols, CPU-GPU coherency, system-level tests, and emulator environments. Develop monitors, checkers, assertions, coverage, and UVM testbenches while collaborating with design, validation, performance, power, and software teams. Guide emulation vendors and support hardware, firmware, and software bring-up.

NVIDIA seeks a Senior Software Engineer to enhance its AI platform using JAX, focusing on performance optimization in deep learning frameworks while collaborating with applied researchers. The role requires strong programming, system design skills, and a proven ability to develop customer-facing solutions.

Design and implement GPU subsystem ASIC modules, owning microarchitecture, RTL development, verification, synthesis, timing closure, and post-silicon validation. Collaborate with architecture, verification, synthesis, timing, and backend teams to deliver clean, fully verified designs. Responsibilities include architectural tradeoffs, ASIC design flow activities, lab debug, and support for complex components such as arbiters, schedulers, interconnects, caches, and bus protocols.

Develop and optimize high-performance sparse linear algebra kernels and libraries for GPUs. Responsibilities include implementing sparse BLAS operations, direct and iterative solvers, preconditioners, and algebraic multigrid algorithms; collaborating with library, QA, product, and external teams; improving performance, quality, maintainability, and technical roadmaps; and supporting GPU architecture optimization and software development practices.

Lead research and engineering of large-scale multimodal AV foundation models and RL-based driving policies. Build and optimize distributed training pipelines, GPU-accelerated simulation and sim-to-real transfer, improve cluster and GPU utilization, and collaborate to deploy models to production and publish top-tier research.

Lead developer relations and research partnerships in 6G and AI-native RAN. Conduct simulations, design and code AI/ML and LLM algorithms for RAN, publish papers and IP, engage 3GPP and AI-RAN bodies, and collaborate with cross-functional teams to drive accelerated-computing solutions.

Develop and improve robotics training workflows using NVIDIA Omniverse and digital twins. Analyze customer and industry processes, integrate Omniverse capabilities, provide feedback to engineering and product teams, collaborate with global customers and partners, and create workflow training videos. The role requires applied robotics experience, simulation expertise, technical communication, and the ability to manage multiple projects across functions.

Define and position datacenter GPU products for AI, ML, and HPC workloads (especially LLMs). Identify customer needs, translate into product value propositions, lead cross-functional go-to-market planning, and create sales collateral while coordinating with engineering, operations, and field teams.

Conduct and publish advanced research in vehicle autonomy, developing algorithms and systems (agent behavior, end-to-end architectures, closed-loop training, safety assurances). Collaborate with internal product teams and external researchers to transfer research into production, open-source results, and present at top conferences.