Deep Learning Framework Communications Engineer

About the Role

NVIDIA is seeking a motivated Deep Learning engineer to integrate advanced communication technologies into AI stacks such as PyTorch, TRT-LLM, vLLM, SGLang, JAX. You will work with the team that created communication libraries like NCCL, NVSHMEM, and technology like GPUDirect. Your work will directly impact AI application performance for diverse multi-GPU demands.

Key Responsibilities

• Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production.
• 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.
• Improve AI compilers to hide communications or perform automatic fusion.
• Conduct in-depth AI workload performance characterization on multi-GPU clusters.
• Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.
• Author custom communication or fused compute-communication kernels to showcase ultimate performance on NV platforms.
• Influence the roadmap of communication libraries - NCCL & NVSHMEM.
• Collaborate with a dynamic team across multiple time zones.

Requirements & Tech Stack

• B.S, M.S. or PHD in Computer Science, or related field (or equivalent experience) with software engineering and HPC/AI experience.
• Development or integration experience with Deep Learning Frameworks such PyTorch, JAX, and Inference Engines such as TRT-LLM, vLLM, SGLang.
• Rapid prototyping and development with Python, C++, CUDA or related DSLs (Triton, cuTe).
• Solid grasp of AI models, parallelisms, and/or compiler technologies (e.g. torch.compile).
• Experience conducting performance benchmarking on AI clusters.
• Familiarity with at least one performance profiler toolchain (PyTorch profiler, NVIDIA Nsight Systems).
• Understanding of HPC/AI communication concepts (1-sided v 2-sided communication, elasticity, resiliency, topology discovery, etc).
• Adaptability and passion to learn new areas and tools.
• Flexibility to work and communicate effectively across different teams and timezones.

Ways To Stand Out

• Experience with parallel programming on at least one communication runtime (NCCL, NVSHMEM, MPI).
• Good understanding of computer system architecture, HW-SW interactions, and operating systems principles (systems software fundamentals).
• Expertise in one or more of these areas: Training, Distributed inference, MoE, Reinforcement Learning, kernel authoring (on CUDA, Triton, cuTe, etc.).
• Experience with programming for compute & communication overlap in distributed runtimes.
• Experience with AI compiler pattern matching and lowering.
• Solid understanding of memory hierarchy, consistency model, and tensor layout.