Engineering Manager, Inference Benchmarking — AI Perf

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +5 · Remote

Engineering Manager for NVIDIA's AIPerf platform, a standard for assessing LLM serving performance. The role involves leading a team to build and advance the platform, focusing on core infrastructure, accuracy of benchmark results, and advising on upstream engine integrations for various AI workloads (LLM, multimodal, diffusion, computer vision). Requires strong systems engineering, inference infrastructure, and open-source community experience.

What you'd actually do

  1. Driving the technical roadmap for AIPerf's core infrastructure: load generation, ZMQ-based microservices, GPU telemetry (DCGM/PyNVML, Prometheus metrics, statistical confidence intervals, and Kubernetes-native deployment.
  2. Taking ownership for the accuracy and statistical soundness of benchmark results that engineering groups throughout the industry depend on to inform production infrastructure decisions.
  3. Advising upstream engine integrations involving vLLM, TRT-LLM, and SGLang in partnership with NVIDIA's Dynamo and NIM teams to maintain AIPerf's relevance across emerging hardware, workload categories, and inference configurations.
  4. Hiring, mentoring, and growing a team of senior engineers operating in a high-velocity open-source environment with active external contributors worldwide.

Skills

Required

  • Bachelor's degree in Computer Science, Electrical Engineering, or related field, or equivalent experience.
  • 8+ overall years of software engineering experience building performance-critical infrastructure, ML tooling, or distributed systems.
  • 3+ years of engineering leadership experience as a tech lead, TLM, or engineering manager.
  • Deep understanding of LLM inference mechanics — TTFT, ITL, KV caching, Prefill/Decode, speculative decoding — and the ability to reason about measurement correctness and reproducibility.
  • Proven track record of collaborating across multi-functional groups and delivering production-quality output in high-velocity, high-external-visibility environments.

Nice to have

  • Extensive experience with vLLM, TRT-LLM or SGLang internals along with contributions to their upstream projects.
  • Experience building Kubernetes-native infrastructure including operators, Helm charts, and GPU observability tooling (DCGM, dcgm-exporter, PyNVML).
  • Background in competitive benchmarking frameworks such as MLPerf or equivalent industry-standard evaluation systems.
  • History leading or making meaningful contributions to active open-source projects with external communities.

What the JD emphasized

  • performance-critical infrastructure
  • ML tooling
  • distributed systems
  • Deep understanding of LLM inference mechanics
  • reason about measurement correctness and reproducibility
  • delivering production-quality output in high-velocity, high-external-visibility environments

Other signals

  • LLM inference mechanics
  • benchmarking platform
  • open-source communities
  • GPU telemetry