Senior Deep Learning Software Engineer, Inference

NVIDIA NVIDIA · Semiconductors · CA +4 · Remote

Senior Software Engineer specializing in Deep Learning Inference to optimize GPU-accelerated software for AI applications. Focus on high-performance open-source frameworks for efficient large-scale model serving and inference, improving platforms for deployment and serving of LLMs.

What you'd actually do

  1. Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
  2. Scale performance of DL models across different architectures and types of NVIDIA accelerators.
  3. Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
  4. Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.

Skills

Required

  • Masters or PhD or equivalent experience in relevant field (Computer Engineering, Computer Science, EECS, AI)
  • 5+ years of relevant software development experience
  • excellent C/C++ programming and software design skills

Nice to have

  • SW Agile skills
  • Python experience
  • Prior experience with training, deploying or optimizing the inference of DL models in production
  • Prior background with performance modeling, profiling, debug, and code optimization or architectural knowledge of CPU and GPU
  • GPU programming experience (CUDA, OAI TRITON or CUTLASS)
  • Contribute to deep learning software projects, such as PyTorch, vLLM, and SGLang
  • Experience with Multi GPU Communications (NCCL, NVSHMEM)

What the JD emphasized

  • optimize the GPU-accelerated software that powers today’s most sophisticated AI applications
  • high-performance open-source frameworks
  • forefront of efficient large-scale model serving and inference
  • improving these platforms, facilitating smooth deployment and serving of groundbreaking language models
  • implement the latest algorithms for public release in inference frameworks
  • identifying and driving performance improvements for state-of-the-art LLM and Generative AI models
  • implement and optimize model serving pipelines

Other signals

  • optimize inference performance
  • deploy and serve LLMs
  • high-performance open-source frameworks