Interested in this AI Product Manager role at NVIDIA?
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We are seeking a Product Manager to lead strategic AI platform initiatives across infrastructure, silicon, and developer experience. You will define product vision and execution across AI observability, profiling, agentic automation, and AI\-native developer workflows. Partnering closely with engineering, architecture, and platform teams, you will deliver AI\-powered capabilities that improve how large\-scale systems are built and operated across the organization.
This is a high\-visibility, high\-ambiguity role requiring strong product judgment, systems thinking, and the ability to drive clarity and alignment across complex cross\-functional initiatives. Hardware Infrastructure serves as the foundational platform for silicon development. We build and operate the systems, environments, and tools that enable hardware engineers to design, simulate, validate, and tape out chips. In addition, we support software teams specifically through our source control platforms, enabling development of new products. Our mission is to accelerate engineering velocity while maintaining the performance, efficiency, and reliability required to deliver world\-class silicon, while ensuring seamless collaboration where hardware and software development intersect.
What You'll Be Doing:
- Define product strategy and execution across a portfolio of AI platform initiatives, translating ambiguous technical challenges into clear problem statements, product direction, and prioritized roadmaps.
- Develop deep empathy for internal developers and engineering teams through direct engagement, turning user challenges into intuitive, high\-impact platform capabilities.
- Drive roadmap planning across short\- and long\-term horizons, balancing customer impact, technical feasibility, scalability, and strategic business priorities.
- Identify and evaluate high\-value opportunities for AI\-powered tooling, agentic automation, and developer productivity improvements, with a strong focus on measurable adoption and operational impact.
- Establish product success metrics and use data\-driven insights to continuously refine prioritization, feature scope, and user experience.
- Champion developer experience and UX quality by understanding real engineering workflows, trust dynamics in AI\-assisted tooling, and the factors that drive sustained platform adoption.
- Communicate product strategy, trade\-offs, and execution plans clearly across technical and executive audiences, influencing decisions at all levels of the organization.
- Lead cross\-functional collaboration across engineering, infrastructure, architecture, and design teams, driving alignment and execution in a highly matrixed environment.
What We Need to See:
- BS or MS in Computer Engineering, Computer Science, or a related technical field, or equivalent experience.
- 12\+ years of product management experience building developer platforms, infrastructure products or AI/ML systems at scale.
- Experience leading products that incorporate LLM\-powered agents, autonomous workflows or AI\-accelerated developer experiences.
- Strong technical depth in AI/ML infrastructure, including distributed training, inference optimization, GPU\-accelerated computing, and large\-scale systems performance.
- Experience defining and delivering observability, telemetry, profiling, or performance tooling for engineering organizations.
- Proven experience in fostering adoption of complex developer\-facing products across large organizations.
- Deep familiarity with modern SDLC tooling and developer workflows across build, test, deployment, and runtime environments.
- Outstanding systems thinking with capacity to reason across the stack \- from hardware and infrastructure layers through APIs, platforms, and end\-user developer experiences.
- Excellent written and verbal communication; able to distill ambiguous technical problems into clear product narratives.
Ways to Stand Out from the crowd:
- Background in semiconductor, hardware, or systems software companies.
- Experience driving enterprise quality AI product adoption \- defining UX, measuring engagement, and working with internal champions.
NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most forward\-thinking and hardworking people in the world on our team and our collaborative talent continues to drive NVIDIA's growth. We are seeking creative and independent engineers with real passion for technology!
\#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.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 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.
Salary Context
This $208K-$327K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $191K across 155 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,824 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At NVIDIA, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 518 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($267K) sits 25% above the category median. Disclosed range: $208K to $327K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
NVIDIA AI Hiring
NVIDIA has 22 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Product Manager, MLOps Engineer. Positions span Austin, TX, US, Santa Clara, CA, US, CA, US. Compensation range: $224K - $379K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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