Senior Software Engineer, AI Inference Systems

NVIDIA • Santa Clara, CA

12d • Hybrid

About The Position

We are seeking highly skilled and motivated software engineers to join us and build AI inference systems that serve large-scale models with extreme efficiency. You’ll architect and implement high-performance inference stacks, optimize GPU kernels and compilers, drive industry benchmarks, and scale workloads across multi-GPU, multi-node, and multi-cloud environments. You’ll collaborate across inference, compiler, scheduling, and performance teams to push the frontier of accelerated computing for AI. What you’ll be doing: Contribute features to vLLM that empower the newest models with the latest NVIDIA GPU hardware features; profile and optimize the inference framework (vLLM) with methods like speculative decoding, data/tensor/expert/pipeline-parallelism, prefill-decode disaggregation. Develop, optimize, and benchmark GPU kernels (hand-tuned and compiler-generated) using techniques such as fusion, autotuning, and memory/layout optimization; build and extend high-level DSLs and compiler infrastructure to boost kernel developer productivity while approaching peak hardware utilization. Define and build inference benchmarking methodologies and tools; contribute both new benchmark and NVIDIA’s submissions to the industry-leading MLPerf Inference benchmarking suite. Architect the scheduling and orchestration of containerized large-scale inference deployments on GPU clusters across clouds. Conduct and publish original research that pushes the pareto frontier for the field of ML Systems; survey recent publications and find a way to integrate research ideas and prototypes into NVIDIA’s software products.

Requirements

• Bachelor’s degree (or equivalent expeience) in Computer Science (CS), Computer Engineering (CE) or Software Engineering (SE) with 7+ years of experience; alternatively, Master’s degree in CS/CE/SE with 5+ years of experience; or PhD degree with the thesis and top-tier publications in ML Systems, GPU architecture, or high-performance computing
• Strong programming skills in Python and C/C++; experience with Go or Rust is a plus
• Solid CS fundamentals: algorithms & data structures, operating systems, computer architecture, parallel programming, distributed systems, deep learning theories
• Knowledgeable and passionate about performance engineering in ML frameworks (e.g., PyTorch) and inference engines (e.g., vLLM and SGLang)
• Familiarity with GPU programming and performance: CUDA, memory hierarchy, streams, NCCL; proficiency with profiling/debug tools (e.g., Nsight Systems/Compute)
• Experience with containers and orchestration (Docker, Kubernetes, Slurm); familiarity with Linux namespaces and cgroups
• Excellent debugging, problem-solving, and communication skills; ability to excel in a fast-paced, multi-functional setting

Nice To Haves

• Experience building and optimizing LLM inference engines (e.g., vLLM, SGLang)
• Hands-on work with ML compilers and DSLs (e.g., Triton, TorchDynamo/Inductor, MLIR/LLVM, XLA), GPU libraries (e.g., CUTLASS) and features (e.g., CUDA Graph, Tensor Cores)
• Experience contributing to containerization/virtualization technologies such as containerd/CRI-O/CRIU
• Experience with cloud platforms (AWS/GCP/Azure), infrastructure as code, CI/CD, and production observability
• Contributions to open-source projects and/or publications; please include links to GitHub pull requests, published papers and artifacts

Responsibilities

• Contribute features to vLLM that empower the newest models with the latest NVIDIA GPU hardware features
• Profile and optimize the inference framework (vLLM) with methods like speculative decoding, data/tensor/expert/pipeline-parallelism, prefill-decode disaggregation
• Develop, optimize, and benchmark GPU kernels (hand-tuned and compiler-generated) using techniques such as fusion, autotuning, and memory/layout optimization
• Build and extend high-level DSLs and compiler infrastructure to boost kernel developer productivity while approaching peak hardware utilization
• Define and build inference benchmarking methodologies and tools
• Contribute both new benchmark and NVIDIA’s submissions to the industry-leading MLPerf Inference benchmarking suite
• Architect the scheduling and orchestration of containerized large-scale inference deployments on GPU clusters across clouds
• Conduct and publish original research that pushes the pareto frontier for the field of ML Systems
• Survey recent publications and find a way to integrate research ideas and prototypes into NVIDIA’s software products

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees