Senior Research Engineer - Enterprise Products

NVIDIA NVIDIA · Semiconductors · WA +2 · Remote

Senior Research Engineer at NVIDIA focused on Generative AI inference, specifically designing and evaluating routing policies for LLM traffic and building agentic benchmarks. The role involves shipping to open-source repositories and collaborating across NVIDIA teams to optimize the accelerated serving stack.

What you'd actually do

  1. Design and evaluate routing policies for LLM traffic to best use mixture of model systems.
  2. Build and run agentic benchmarks (e.g., Terminal-Bench ) to measure algorithm quality, and turn results into calibration data and routing profiles
  3. Ship to an open-source repo: design docs, code review, docs, and community contributions
  4. Collaborating with engineering teams across all of NVIDIA to ensure our software integrates seamlessly up and down the NVIDIA accelerated serving stack.

Skills

Required

  • Bachelor's of Master's degree in Computer Science or equivalent experience.
  • 8+ years of industry experience in Deep Learning frameworks (PyTorch or TensorFlow).
  • Experience designing or running LLM evaluations/benchmarks — ideally agentic ones — and drawing statistically sound conclusions from them
  • Understanding of modern techniques in Machine Learning, Deep Neural Networks, Natural Language Processing, or Speech Recognition.
  • Empirical research mindset: forming hypotheses about new algorithms, running calibrations, iterating on results
  • Strong communication and interpersonal skills, along with the ability to work in a dynamic and distributed team.
  • Strong computer science fundamentals - algorithms and data structures, computational complexity, parallel and distributed computing, system software.

Nice to have

  • A history of mentoring junior engineers and interns is a huge plus.
  • A desire to constantly grow and learn new things.
  • Experience architecting or developing large-scale distributed systems for deep learning.
  • Agentic benchmark creation and publications.
  • Knowledge of CPU and/or GPU architecture.
  • GPU programming (CUDA).

What the JD emphasized

  • Experience designing or running LLM evaluations/benchmarks — ideally agentic ones — and drawing statistically sound conclusions from them

Other signals

  • Generative AI inference
  • LLM traffic routing
  • agentic benchmarks
  • open-source contributions