Senior Product Manager – AI Inference Performance

• Own the inference performance roadmap. Set direction across the stack: how models are represented, how memory and state are managed, how requests are scheduled and served, and how tokens get generated. The techniques change fast. Judge which ones matter, then decide what we build, what we adopt, and what we retire.
• Build platforms, not one-offs. Deliver capabilities that generalize across model families, deployment topologies, and customer sizes. Build for easy adoption, sane defaults, and extensibility.
• Agentic and Multi-Turn Workloads: Define the performance strategy for agentic applications, where long-running sessions, tool-call stalls, and unexpected output lengths break the assumptions built into single-turn serving. Drive capabilities around cross-turn cache reuse, request prioritization, and efficient handling of idle time in agent loops.
• Framework & Ecosystem Strategy: Define how our optimizations land across TensorRT-LLM, vLLM, SGLang, and NVIDIA Dynamo. Partner with open-source communities and internal engineering teams so customers get great performance on NVIDIA hardware.
• Benchmarking & Performance Claims: Own how performance is measured, published, and reproduced. Define the benchmark methodology, the metrics that matter (TTFT, ITL, throughput per GPU, cost per million tokens), and the guardrails that keep our numbers credible.
• Run the product day to day. Own release readiness, quality bars, regression tracking, customer blocking issues, and the feedback loop from production deployments back into the roadmap.

• 12+ years in product management at a technology company, or comparable time as a founder, engineering lead, or technical product owner.
• Hands-on experience in AI/LLM Inference Performance Optimization in an engineering or product management capacity including deep familiarity with techniques like KV caching and reuse, quantization, speculative decoding, disaggregated serving
• Familiarity with the inference and orchestration frameworks customers use: TensorRT-LLM, vLLM, SGLang, NVIDIA Dynamo, and the surrounding serving and scheduling ecosystem.
• Proven track record of working independently — you can take an ambiguous problem space, define the strategy, and drive it to a shipped outcome without waiting to be told what to do next.
• Operational experience running a live product: release management, quality and regression rigor, customer issues, and support processes.
• Skill at translating low-level capability into business value — lower TCO, faster response, better GPU utilization — for engineers and executives alike.
• BS, MS, or PhD in Computer Science, Computer Engineering, or another relevant area of study (or equivalent experience).

Ways to Stand Out From the Crowd

• Engineering experience with LLM inference performance: profiling, kernel-level or serving-level optimization, or building a serving stack!
• Open-source contributions or product leadership in vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or Dynamo. Production experience at scale counts too: capacity planning, autoscaling, SLA management, or stateful multi-turn applications.
• A habit of reading the relevant research and translating it into roadmap decisions — you have intuition for where model architectures and serving techniques are heading next!

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 208,000 USD – 327,750 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.