> Markdown version of [/jobs/ext/3409252-product-manager-inference-platform](https://www.wearedevelopers.com/jobs/ext/3409252-product-manager-inference-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Product Manager, Inference Platform - **Company:** NVIDIA Ltd. - **Location:** Santa Clara, United States - **Experience:** Expert - **Salary:** $208,000.0 - $327,750.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Cloud Computing, Open Source Technology, Management of Software Versions, Large Language Models, Machine Learning Operations, TensorRT, Decoding - **Published:** September 30, 2026 - **Apply:** https://startup.jobs/senior-product-manager-inference-platform-2100-nvidia-usa-10220336 ## About the Role * 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. ## Description 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. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices](https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)