Senior Deep Learning Framework Communications Engineer
US$152,000 – US$287,500 / yr base salary
Posted 27 Aug 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Contract
- Full-time
| Base salary | US$152,000 – US$287,500 / yr |
|---|
Field of studyComputer Science (or related)
Study levelBachelor's or higher
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.
We are looking for a motivated Deep Learning engineer to bring advanced communication technologies into AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, etc. You will be working with the team that created communication libraries like NCCL, NVSHMEM & technology like GPUDirect -- for scaling Deep Learning and HPC applications. Your customers will have diverse multi-GPU demands, ranging from training on scales up to 100K GPUs to inference down at microsecond latency. Communication performance between the GPUs has a direct impact on AI applications. Your work in AI toolkits will make all of those easier for the community. This is an outstanding opportunity for someone with an AI background to advance the state of the art in this space. Are you ready to contribute to the development of innovative technologies and help realize NVIDIA's vision?
What you will be doing:
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Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production
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Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities. Collaborate hands-on with teams working on the latest AI models.
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Improve AI compilers to hide communications or perform automatic fusion.
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Conduct in-depth AI workload performance characterization on multi-GPU clusters.
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Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.
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Author custom communication or fused compute-communication kernels to showcase ultimate performance on NV platforms.
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Influence the roadmap of communication libraries - NCCL & NVSHMEM.
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Collaborate with a very dynamic team across multiple time zones.
What we need to see:
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B.S, M.S. or PHD in Computer Science, or related field (or equivalent experience) with 5+ software engineering and HPC/AI experience
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Development or integration experience with Deep Learning Frameworks such PyTorch, JAX, and Inference Engines such as TRT-LLM, vLLM, SGLang