Top Tech Jobs & Startup Jobs

Develop, debug, and harden kernel-mode GPU and SoC system software across design, emulation, bringup, and production. Focus on performance, power optimization, robustness, and cross-team collaboration for datacenter, desktop, workstation, and console platforms.

Translate cutting-edge agent research into secure, production-grade runtime systems: prototype, benchmark, reproduce methods, build agent harnesses and evaluation/red-team workflows, secure agent toolchains, and partner across research, product, and engineering to integrate features into OpenShell.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Design, implement and verify boot firmware for flagship CPU SoCs. Define and decompose functional and safety requirements, perform FMEA/DFA, validate on pre-silicon simulation, support silicon bring-up and productization, and collaborate across teams to meet safety and security standards.

Lead global sales efforts to drive adoption of NVIDIA CPU solutions across OEMs, hyperscalers, cloud providers, and AI-native customers. Build and scale pipeline, execute strategic deals, enable field teams, provide pipeline visibility, and align cross-functional partners to translate CPU strategy into revenue growth.

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

As a Principal Software Engineer, you'll develop software systems for NVIDIA's rack-scale infrastructure, defining software architecture, collaborating across teams, mentoring engineers, and ensuring high-quality technical decisions in complex environments.

Lead technical and operational programs for enterprise AI partners. Define schedules, translate customer requirements, drive cross-functional execution from design to production, manage risks and post-deployment support, and coordinate global teams to integrate complex AI model stacks into customer environments.

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal teams.