Level

NVIDIA

Senior Product Manager, Inference Platform

AI in this role

Define vision and strategy for large-scale AI model inference platform capabilities and serving infrastructure at NVIDIA.

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ml-opsai-researchproduct-managementmodel-servinginferenceai-infrastructure

At NVIDIA, we are building the foundation & blueprints for how AI workloads are served at scale for NVIDIA employees and DSX Partners. As a Product Manager for Inference Platform, you will help define and drive the products and platform capabilities that enable large-scale models serving across a broad portfolio of models, both open source and proprietary. You will work at the intersection of AI research, infrastructure engineering, and real user needs, shaping how inference is delivered reliably, efficiently, and at scale.


This is high-impact role. You will be expected to bring structure to undefined problem spaces, make progress without complete information, and build conviction through deep engagement with users, engineers, and the broader ecosystem of AI Cloud partners. The right candidate is equally comfortable discussing model serving architecture, token economics and writing a clear product brief.


What you will be doing:

  • Define product vision and strategy for inference platform capabilities — including APIs, capacity management, cost management, performance and optimization, and model serving infrastructure.
  • Translate user needs and infrastructure constraints into clear requirements and prioritized roadmaps.
  • Partner closely with engineering, research, and user groups teams to drive execution from concept through launch.
  • Be responsible for end-to-end product lifecycle for inference-related products and platform investments.
  • Develop deep understanding of the inference ecosystem — model formats, serving frameworks, API formats, and the tradeoffs that matter at scale.
  • Drive clarity in ambiguous situations by framing the problem, identifying what is known and unknown, and proposing a path forward.
  • Represent the voice of the user and ensure product decisions are grounded in real needs, not assumptions.
  • Track and synthesize developments across the inference landscape: open source model releases, serving frameworks, competitive dynamics, and emerging use cases.

What we need to see:

  • 12+ years of experience with a track record of delivering complex technical products.
  • Bachelors degree or higher, or equivalent experience
  • Strong written and verbal communication, you are able to write clear, concise product documents, specs, and strategies.
  • Ability to operate in ambiguous, fast paced environments and make progress without a full playbook.
  • Analytical professional who can break down complex problems, identify the right questions, and drive toward decisions.
  • Strong user empathy — able to synthesize qualitative and quantitative signals into a coherent picture of what users need and why.
  • Deep familiarity with AI/ML systems and inference serving frameworks (such as TensorRT-LLM, vLLM, or Triton Inference Server), tradeoffs involved, and what matters to model publishers, application developers and cloud operators.
  • Experience with inference APIs- design, versioning, performance, hardware efficiency, and developer experience.
  • Familiarity with open source as well as commercial model ecosystems and the different considerations each brings to a serving platform.

Ways to stand out from the crowd:

  • Experience with sophisticated inference techniques: disaggregated prefill/decode, KV cache management, speculative decoding, or continuous batching.
  • Background in large scale systems and developer platforms, cloud infrastructure, or MLOps tooling.
  • Exposure to capacity planning, quota management, or resource scheduling in large-scale compute environments.

You thrive in the early stages of building — where the problem is not fully defined, the team is still forming, and the decisions you make will shape direction for years. You don't wait for perfect information. You ask good questions, build conviction incrementally, and create momentum. You can balance user's reality and engineering constraints simultaneously. You bring clarity and cut through noise. You have a genuine curiosity about how inference works. You care about users’ needs beyond trivia, as it improves your product.


#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 208,000 USD - 327,750 USD for Level 5, and 240,000 USD - 379,500 USD for Level 6.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 2, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

How we rate this

Senior Product Manager, Inference Platform at NVIDIA rates 80 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsAI ResearchProduct ManagementModel ServingInferenceAI InfrastructurevLLM

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. Tell me about a research question you investigated. What did you find?
  3. Tell me about a project where product management was part of your work. What did you do?
  4. Tell me about a project where model serving was part of your work. What did you do?
  5. Tell me about a project where inference was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: ML Ops, AI Research, Product Management, Model Serving, and Inference. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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