11 set - Italia
NVIDIA
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.
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.
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