What we need to see: B.Sc., M.Sc., or equivalent experience in Computer Science or Computer Engineering 5+ years of hands-on software engineering experience in performance-critical systems Solid understanding of deep learning architectures (Transformers, SSMs, MoE, …) Experience with systems where hardware constraints matter: GPU programming, memory hierarchy, networking, or distributed computing Strong software engineering fundamentals: clean design, extensibility, testability. Good judgment about when complexity is warranted Effective communicator who works well across teams and time zones Experience optimizing deep learning workloads on NVIDIA GPUs using roofline models, Nsight/PyTorch profilers and end-to-end traces Ways to stand out from the crowd: Contributions to open-source inference runtimes and libraries - vLLM, SGLang, FlashInfer, Dynamo or similar Hands-on work with LLM quantization (FP8, NVFP4, MXFP8, mixed-precision) and practical understanding of numerical precision tradeoffs Track record with distributed inference at scale: tensor parallelism, pipeline parallelism, expert parallelism, disaggregation, multi-node orchestration Deep knowledge of the latest LLM architectural trends: multi-token predictors, sparse hybrid models, attention and state-space mechanisms Experience with performance modeling and simulation-to-silicon correlation NVIDIA is widely considered one of the world's most desirable employers in the technology field. We have some of the most forward-thinking and hardworking people working for us. If you're creative and autonomous, we want to hear from you! We are committed to fostering a diverse work environment and are proud to be an equal-opportunity employer. We highly value diversity in our current and future employees. We do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, vet
