Senior Software Engineer, AI Inference Systems

NVIDIA; France; Hybride; EUR 120 000 - 190 000; Plein temps

Résumé du poste

NVIDIA in France is seeking a senior ML systems engineer to advance large-scale AI inference on GPU hardware. You will contribute features to vLLM, optimize inference pipelines, and help push the pareto frontier of performance.

You’ll design and benchmark GPU kernels, build high-level DSLs, and orchestrate containerized deployments on GPU clusters across clouds. Strong Python/C++ skills and deep knowledge of ML systems are required.

Qualifications

• Bachelor’s degree (or equivalent) in CS/CE/SE with 7+ years, or Master with 5+ years, or PhD in ML Systems/GPUs.
• Strong Python and C/C++ programming; Go or Rust a plus; solid CS fundamentals.
• Knowledgeable about performance engineering in ML frameworks and inference engines.

Responsabilités

• Contribute features to vLLM and optimize inference with new NVIDIA GPU features.
• Develop, optimize, and benchmark GPU kernels with fusion, autotuning, and memory/layout optimizations.
• Define and build inference benchmarking methodologies; contribute MLPerf submissions.
• Architect scheduling and orchestration of containerized large-scale inference on GPU clusters across clouds.
• Publish original research pushing the pareto frontier in ML Systems and integrate ideas into NVIDIA software.

Connaissances

Python

C/C++

Go

Rust

Algorithms

Operating Systems

Computer Architecture

Parallel Programming

Distributed Systems

DL Theories

Formation

Bachelor’s degree in CS/CE/SE

Master’s degree in CS/CE/SE

PhD in ML Systems/GPUs

Outils

Nsight Systems/Compute

CUDA

NCCL

Docker

Kubernetes

Slurm

Linux namespaces

cgroups

Triton

MLIR/LLVM

XLA

Description du poste

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.

What we need to see:

• 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.

Ways to stand out from the crowd

• 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.

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’re excited to build systems, kernels, and tools that make large-scale AI faster, more efficient, and easier to deploy, we’d love to hear from you.

#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 292,500 PLN - 507,000 PLN for Level 4, and 375,000 PLN - 650,000 PLN for Level 5.