Senior Product Manager - AI & AI Infrastructure
VanHack
Seattle, WA · Posted October 29, 2026
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Where Seattle, WA ranks
Where Seattle, WA placed in our monthly rankings, month by month. Each month stays online at its own address.
November 2026
- #1Cities with the most jobs that do not need a four-year degree in the Pacific Northwest316 jobs · of 8
- #1Best cities for software and engineering jobs in the Pacific Northwest1,776 openings · of 14
- #2Best cities for energy and utilities pay after cost of living$180k · of 18
- #2Best cities for entry-level jobs that want AI skills in the Pacific Northwest17% · of 12
- #2Best cities for AI-skill jobs in the Pacific Northwest26% · of 17
- #2Best cities for software and engineering jobs in the West Coast1,776 openings · of 60
Rankings about product jobs
- Best cities for product jobs #1 New York (738 openings)
- Best cities for product pay after cost of living #1 Santa Clara ($224k)
- Most requested AI skills in product jobs #1 AI product management (17.9%)
- AI tools named most often in product jobs #1 Claude (3.2%)
- Employers with the most product jobs #1 Amazon (356 roles)
Pay compared with the market
This listing does not state pay. Across 264 open senior product manager listings that state pay, the middle half runs $160,500 to $200,000 and the median is $180,000.
Listed pay is what employers advertise, not what people are paid.
Senior Product Manager salaries by state · Check your own pay
How AI is used in this kind of work
Among marketing managers, AI is already used for a large share of the tasks. Measured exposure for marketing managers: 32% (high).
Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.
Skills this job asks for
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About the job
About the role
Our hiring partner is seeking a Senior Product Manager - AI & AI Infrastructure to lead product strategy and execution for cloud platforms serving AI workloads. This role requires demonstrated ownership of AI or AI infrastructure products, not simply general product management experience or use of AI productivity tools.
Location and work arrangement
This is a hybrid role available to candidates based in the Seattle area or the San Francisco Bay Area. Seattle is preferred; Bay Area candidates are welcome.
Key responsibilities
Own product vision, strategy, prioritization and roadmaps for AI and cloud infrastructure capabilities.
Translate enterprise customer needs and AI developer workflows into clear product requirements and measurable outcomes.
Partner with engineering on infrastructure supporting model training, post-training, inference or agentic workloads, balancing performance, reliability, scalability and cost.
Lead discovery, competitive analysis, product launches and adoption initiatives across technical and commercial stakeholders.
Define product success metrics and use customer feedback and workload insights to guide investment decisions.
Communicate technical trade-offs and product direction clearly to customers, engineers and executives.
Required qualifications
Strong senior-level product management experience with demonstrated leadership of product strategy and end-to-end delivery.
A track record of owning AI or AI infrastructure products, with clear examples of customer impact and product outcomes.
Experience with cloud infrastructure and platforms such as AWS or Google Cloud Platform; both providers are not required.
Relevant exposure to at least one of model training, post-training, inference or agentic AI workflows. Experience in all four areas is not required.
Ability to evaluate infrastructure performance, scalability, reliability and cost trade-offs with engineering teams.
Strong customer discovery, prioritization and cross-functional communication skills.
Based in the Seattle area or San Francisco Bay Area and available for hybrid work.
Preferred technical context
GPU infrastructure, accelerated computing or AI compute platforms.
Kubernetes, Docker and container orchestration for AI workloads.
Familiarity with model serving, compute scheduling, resource utilization or distributed systems.
Technical familiarity is assessed from a product-management perspective; this is not a software engineering role, and proficiency in Go or Python is not required.