Jobs at NVIDIA

Build and operate automation and tooling for large-scale NVIDIA GPU infrastructure. Responsibilities include bare-metal provisioning, hardware validation, firmware and software upgrades, cluster lifecycle management, BMC and Redfish integration, failure diagnosis, automated recovery, production handoff, on-call support, incident response, and root-cause analysis across hardware, networking, Linux, Kubernetes, and distributed services.

Build and scale software infrastructure for GPU asset provisioning, data center management, monitoring, lifecycle automation, and distributed system testing. Develop reliable platforms across hardware, networking, cloud providers, and AI training applications. Work with GPU diagnostics, cluster and network telemetry, Kubernetes or SLURM, fault tolerance, state management, and system architecture to support large-scale machine learning infrastructure.

Designs, builds, maintains, and deploys large-scale HPC and AI hybrid computing solutions for NVIDIA partners and customers. Guides adoption of end-to-end Agentic AI solutions using NVIDIA compute, networking, and software stacks. Provides technical leadership through demonstrations, proofs of concept, documentation, and debugging while collaborating with executives and engineering teams. Requires expertise in cluster management, containers, Kubernetes, Slurm, Ansible, networking, storage, and cloud-based infrastructure.

Build and lead development of NVIDIA’s open-source NeMo Core Platform for executing, evaluating, deploying, and optimizing agentic AI systems across local, Docker, Kubernetes, Slurm, on-premises, and air-gapped environments. Responsibilities include designing platform APIs, plugin architecture, job orchestration, SDKs, reliability and observability capabilities, testing, performance improvements, technical leadership, code reviews, mentoring, and cross-team problem solving.

Build and operate CI/CD, testing, release, and GPU-enabled infrastructure for NVIDIA Isaac Sim. Responsibilities include managing cloud and on-premises runners, integrating automated simulation and performance tests, optimizing build pipelines, creating reproducible environments and artifacts, monitoring infrastructure health, troubleshooting complex software and GPU issues, and strengthening release security and traceability.

Develop high-performance machine learning inference runtimes and compiler components for NVIDIA platforms. Optimize neural network graph mappings through compiler transformations, scheduling, memory layouts, profiling, and benchmarking. Collaborate with hardware architects on codesign, evaluate spatial accelerator techniques, and contribute to NVIDIA’s software ecosystem. The role also involves research, technical publications, and presentations on compilation and inference optimization.

Verify inference accelerator ASIC designs at block and SoC levels using advanced methodologies, random stimulus, functional coverage, assertion-based verification, SystemVerilog, and UVM. Develop testbenches, verification infrastructure, reference models, and automated flows; debug designs; collaborate with architects, designers, and pre- and post-silicon teams; and improve verification productivity through innovative techniques.

As a Senior Formal Verification Engineer, you will verify ASICs using formal methods, collaborate multi-functionally, lead methodology enhancements, and mentor junior engineers while ensuring compliance and correctness.

Design and develop high-speed mixed-signal interfaces and analog circuits for next-generation NVLINK products. Responsibilities include circuit architecture, transistor-level schematic design, layout guidance, verification, simulation, optimization, silicon characterization, and post-silicon debugging. The role requires deep-submicron CMOS and FinFET experience, analog simulation expertise, behavioral modeling, testbench development, and collaboration with layout and engineering teams.