Senior DL Algorithms Engineer, Inference Performance

Nvidia

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Work type
On-site
Location
Santa Clara, CA
Employment
Full-time
Posted
175 days ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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TL;DR · Senior DL Algorithms Engineer, Inference Performance

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The Senior DL Algorithms Engineer - Inference Performance joins the team to optimize LLM and Omni model performance. This role focuses on squeezing every clock cycle out of deep learning workloads by working across the hardware and software stack, from GPU architecture to deep learning frameworks. Key responsibilities include enabling and optimizing open models like Nemotron and Cosmos on the accelerated inference software stack, contributing production code to frameworks such as TRT-LLM, vLLM, SGLang, and FlashInfer, and profiling bottlenecks to push performance boundaries. Candidates must possess a PhD or equivalent experience, proficiency in PyTorch, and a deep understanding of computer architecture and GPU fundamentals. The work involves benchmarking state-of-the-art offerings, performing competitive analysis, and co-designing next-generation AI models and services within the inference performance domain.

Skills

Deep Learning PyTorch CUDA TRT-LLM vLLM SGLang FlashInfer GPU Architecture Performance Profiling LLM OpenCL HPC Computer Architecture Neural Networks

What you'll do

  • Optimize state-of-the-art open models on NVIDIA’s accelerated inference software stack.
  • Contribute new features and production code to open-source frameworks like TRT-LLM and vLLM.
  • Profile and analyze bottlenecks across the full inference stack to improve performance.
  • Benchmark state-of-the-art offerings and perform competitive analysis for the software and hardware stack.
  • Co-design the next generation of AI models and services with partner teams.
  • Perform performance analysis and optimization for GPU-based deep learning workloads.

What we're looking for

  • PhD in CS, EE, or CSEE or equivalent experience.
  • 3+ years of experience.
  • Strong background in deep learning and neural networks, particularly inference.
  • Experience with performance profiling, analysis, and optimization for GPU-based applications.
  • Proficiency in PyTorch or equivalent frameworks for AI or HPC-heavy application development.
  • Deep understanding of computer architecture and familiarity with GPU architecture fundamentals.
  • Proven experience with processor and system-level performance optimization (preferred).
  • Deep understanding of modern LLM/Diffusion architectures (preferred).

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