NVIDIA has been at the forefront of the deep learning revolution, pioneering innovations that have transformed the entire field. As the leading provider of GPUs and AI computing platforms, NVIDIA has empowered researchers and engineers worldwide to accelerate breakthroughs in artificial intelligence.We seek a versatile Senior Software Engineer who is passionate about performance optimization and generative AI. Our team brings the latest research in LLM inference — from novel decoding strategies to quantization schemes — into production across NVIDIA's hardware lineup, from large data center servers to powerful edge devices. We work on the most advanced architectures in the field, with a focus on NVIDIA's own.What you'll be doing:Implement and optimize inference algorithms for LLM and omnimodal architectures, including hybrid Mamba-Transformer and mixture-of-experts modelsProfile inference pipelines using NVIDIA's profiling and simulation tools. Correlate simulation predictions against real hardware across data center and edge devicesWrite and tune GPU kernels (CUDA, Triton) for operators like fused MoE layers, SSM state updates, and quantized GEMMsSolve distributed inference problems: expert parallelism, communication-compute overlap, collective tuning, multi-node deploymentBuild production-grade software inside major open-source libraries - vLLM, SGLang, Dynamo, FlashInferOwn optimization features end-to-end, from scoping through delivery, collaborating with research, product, and engineering teams worldwideWhat we need to see:B.Sc., M.Sc., or equivalent experience in Computer Science or Computer Engineering5+ years of hands-on software engineering experience in performance-critical systemsSolid understanding of deep learning architectures (Transformers, SSMs, MoE, …)Experience with systems where hardware constraints matter: GPU programming, memory hierarchy, networking, or distributed computingStrong software engineering fundamentals: clean design, extensibility, testability. Good judgment about when complexity is warrantedEffective communicator who works well across teams and time zonesExperience optimizing deep learning workloads on NVIDIA GPUs using roofline models, Nsight/PyTorch profilers and end-to-end tracesWays to stand out from the crowd:Contributions to open-source inference runtimes and libraries - vLLM, SGLang, FlashInfer, Dynamo or similarHands-on work with LLM quantization (FP8, NVFP4, MXFP8, mixed-precision) and practical understanding of numerical precision tradeoffsTrack record with distributed inference at scale: tensor parallelism, pipeline parallelism, expert parallelism, disaggregation, multi-node orchestrationDeep 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 correlationNVIDIA 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, veteran status, disability status, or any other characteristic protected by law.