Software Engineer, AI Systems - vLLM and MLPerf

We are seeking highly skilled and motivated software engineers to join our vLLM & MLPerf team. You will define and build benchmarks for MLPerf Inference, the industry-leading benchmark suite for inference system-level performance, as well as contribute to vLLM and optimize its performance to the extreme for those benchmarks on bleeding-edge NVIDIA GPUs.

What you’ll be doing:

• Design and implement highly efficient inference systems for large-scale deployments of generative AI models.

• Define inference benchmarking methodologies and build tools that will be adopted across the industry.

• Develop, profile, debug, and optimize low-level system components and algorithms to improve throughput and minimize latency for the MLPerf Inference benchmarks on bleeding-edge NVIDIA GPUs.

• Productionize inference systems with uncompromised software quality.

• Collaborate with researchers and engineers to productionize innovative model architectures, inference techniques and quantization methods.

• Contribute to the design of APIs, abstractions, and UX that make it easier to scale model deployment while maintaining usability and flexibility.

• Participate in design discussions, code reviews, and technical planning to ensure the product aligns with the business goals.

• Stay up to date with the latest advancements and come up with novel research ideas in inference system-level optimization, then translate research ideas into practical, robust systems. Explorations and academic publications are encouraged.

What we need to see:

• Bachelor’s, Master’s, or PhD degree in Computer Science/Engineering, Software Engineering, a related field, or equivalent experience.

• 5+ years of experience in software development, preferably with Python and C++.

• Deep understanding of deep learning algorithms, distributed systems, parallel computing, and high-performance computing principles.

• Hands-on experience with ML frameworks (e.g., PyTorch) and inference engines (e.g., vLLM and SGLang).

• Experience optimizing compute, memory, and communication performance for the deployments of large models.

• Familiarity with GPU programming, CUDA, NCCL, and performance profiling tools.

• Ability to work closely with both research and engineering teams, translating state-of-the-art research ideas into concrete designs and robust code, as well as coming up with novel research ideas.

• Excellent problem-solving skills, with the ability to debug complex systems.

• A passion for building high-impact software that pushes the boundaries of what’s possible with large-scale AI.

Ways to stand out from the crowd:

• Background in building and optimizing LLM inference engines such as vLLM and SGLang.

• Experience building ML compilers such as Triton, Torch Dynamo/Inductor.

• Experience working with cloud platforms (e.g., AWS, GCP, or Azure), containerization tools (e.g., Docker), and orchestration infrastructures (e.g., Kubernetes, Slurm).

• Exposure to DevOps practices, CI/CD pipelines, and infrastructure as code.

• Contributions to open-source projects (please provide a list of the GitHub PRs you submitted).

At NVIDIA, we believe artificial intelligence (AI) will fundamentally transform how people live and work. Our mission is to advance AI research and development to create groundbreaking technologies that enable anyone to harness the power of AI and benefit from its potential. Our team consists of experts in AI, systems and performance optimization. Our leadership includes world-renowned experts in AI systems who have received multiple academic and industry research awards. If you've hacked the inner workings of PyTorch, or if you've written many CUDA/HIP kernels, or if you've developed and optimized inference services or training workloads, or if you've built and maintained large-scale Kubernetes clusters, or if you simply just enjoy solving hard problems, feel free to drop an application!

#LI-Hybrid