What we need to see:
A Masters Degree in Computer Science, Computer Engineering, Electrical Engineering, related STEM field or equivalent experience.
5+ years of relevant work experience
Strong proficiency in modern C++ (design, implementation, debugging, and performance considerations).
Experience designing, maintaining, and refactoring software libraries and APIs with long-term support in mind.
Comfort working in large, multi-repository or multi-component codebases with layered dependencies.
Demonstrated ability to lead or drive triage of difficult reliability issues and produce clear root-cause analysis.
Ability to clearly communicate software architecture and design tradeoffs, including using diagrams and written design docs.
Low-level platform software experience (e.g., firmware/boot flows, RTOS, BMCs/MCUs, RISC-V, or closely related system software).
Linux systems experience that includes driver or kernel-adjacent interfaces (e.g., VFIO or similar subsystems).
Hardware bring-up and/or system triage experience (fault analysis, system diagnostics, or validation support in lab environments).
Ways to stand out from the crowd:
Distributed systems experience (e.g., MPI, gRPC, RPC frameworks, coordination/telemetry patterns).
Experience with inference systems and token serving (e.g., vLLM or similar serving/runtime stacks).
Experience shipping and supporting customer-facing SDKs, including documentation and ABI compatibility practices.
Production readiness and delivery experience (e.g., CI/CD and release workflows, monitoring/alerting practices, Kubernetes and/or data center operational workflows).
The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA’s GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Today, NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.”
Widely considered to be one of the technology world’s most desirable employers, NVIDIA has some of the most forward-thinking and hardworking people in the world inventing the future with us. Are you a creative and collaborative software engineer seeking new challenges? If so, we want to hear from you! Come, join us and help build the real-time, cost-effective AI computing platform driving our success in this exciting and quickly growing field.
