Top Tech Jobs & Startup Jobs

Design next-generation GPU-accelerated computing systems and architectures for cloud, data center, deep learning, analytics, and high-performance computing applications. Develop analytical models, simulators, and visualization tools to evaluate performance, cost, and efficiency trade-offs. Analyze hardware-software interactions in large-scale deployments and collaborate with software, product, and business teams to guide GPU cloud computing strategy.

Lead emulation-based SoC verification: bring up SoCs on emulators, root-cause test and environment failures, collaborate with cross-functional teams, debug with emulation vendors, and maintain accelerated UVM testbenches using SystemVerilog, Verilog/VHDL and C/C++ DPI transactors.

Develop and lead production software-defined networking for NVIDIA’s global GPU cloud infrastructure. Design multi-tenant control and data planes using Open vSwitch, OVN, OpenFlow, Kubernetes, C, and Go. Build networking services, plugins, automation, testing, observability, and reliability improvements. Lead architecture and code reviews, mentor engineers, support on-call operations, and resolve complex production incidents through diagnostics and source-level debugging.

Develop embedded platform software for NVIDIA Jetson, IGX, and DGX Spark products. Responsibilities include hardware bring-up, boot firmware, Linux kernel device drivers, RTOS firmware, software SDKs, platform validation, debugging, and production deployment across the full product lifecycle. The role requires collaboration with hardware and global engineering teams to deliver system-level solutions for AI-at-the-edge platforms.

Own production Slurm support cases for AI and HPC customers, diagnosing scheduling, resource allocation, accounting, authentication, networking, storage, database, GPU, and high-availability issues. Advise on configuration, upgrades, operations, and incident recovery; investigate performance and scalability problems; collaborate with engineering on defect reports and reproductions; and develop technical guides, diagnostic tools, and training. The role requires extensive production Slurm administration experience and expert Linux systems knowledge.

The Senior Production Engineer will scale AI infrastructure, manage production systems, implement monitoring capabilities, and ensure reliability and performance across NVIDIA's AI clusters.

Build and scale robotics simulation and sim-to-real workflows for NVIDIA partners. Develop solutions involving robot rigging, physics, rendering, sensor simulation, synthetic data, GPU computing, and learned policies. Lead technical evaluations, proofs of concept, workshops, and reference implementations; diagnose performance and integration issues; validate hybrid physics and neural approaches; and collaborate with research, engineering, product, and customers to guide platform adoption and roadmaps.

Lead business operations and analytics across NVIDIA’s ODM ecosystem. Build dashboards and operational models tracking capacity, yield, and shipments; connect metrics to financial outcomes; establish governance and standardized reviews; improve data accuracy, process automation, and operational visibility across global manufacturing partners. Collaborate with finance, engineering, operations, product teams, and ODMs to drive scalable processes and measurable supply-chain results.

Leads site reliability operations and Tier 3 enterprise support across identity, messaging, compute, endpoints, databases, networking, and datacenter infrastructure. Owns incidents, SLAs, compliance, vulnerability remediation, asset management, root-cause analysis, automation, and permanent fixes. Provides technical leadership and mentoring, supports executives, coordinates with global platform teams and vendors, leads site projects and major incidents, and represents local priorities in regional and global IT initiatives.

Designs and deploys large-scale GPU infrastructure for public-sector customers. Leads solution architecture across compute, storage, networking, cluster deployment, integration, performance optimization, and troubleshooting. Acts as a customer technical advisor, coordinates engineering and product teams, delivers technical presentations and workshops, and supports customer deployments at data centers. Requires expertise in accelerated computing, AI infrastructure, high-performance networking, schedulers, benchmarking, and Kubernetes.