(Senior)Product Manager - AI Infrastructure

The Role

We are hiring a Product Manager or Senior Product Manager to own the end-to-end lifecycle of our AI inference product direction. This includes market discovery and competitive intelligence, requirements definition, roadmap execution, and go-to-market readiness. You will partner closely with Engineering and Research and collaborate with Marketing and Sales to translate technical differentiation into clear product narratives and measurable customer value.

Key Responsibilities

• Market research & strategy: build a sharp understanding of the AI inference and LLM market (buyer needs, adoption patterns, evaluation criteria, and emerging trends)
• Competitive & ecosystem analysis: maintain an up-to-date view of competitors and alternatives (chips, systems, cloud offerings, and inference stacks)
• translate competitive insights into positioning, requirements, and roadmap priorities
• Requirements & roadmap ownership: work with Engineering and Research to define product requirements, milestones, and success metrics
• drive roadmap prioritization with clear tradeoffs and rationale
• ensure execution stays connected to customer needs and measurable outcomes
• GTM + sales enablement: partner with Marketing on messaging, launches, and content strategy
• support Sales with enablement assets: pitch narratives, competitive battlecards, FAQs, case studies, and evaluation guidance
• Technical storytelling & content: create crisp, credible technical content (blogs, whitepapers, solution briefs, product docs, and presentations)
• articulate “why we win” clearly to technical and business stakeholders
• Hands-on analysis: use data to drive decisions: analyze benchmarks, workload characteristics, and performance/efficiency tradeoffs
• build lightweight Python tools/scripts to validate assumptions and support product decisions

Qualifications

The more of the following qualifications you bring, the stronger the fit for this role:

1. Strong semiconductor and accelerator expertise, including familiarity with GPUs and TPUs, and an understanding of how customers evaluate hardware and system trade-offs such as performance, throughput, latency, deployability, and operational efficiency.
2. Strong machine learning depth, ideally supported by a university degree / PhD or significant experience in ML, AI systems, or a related area, with a solid understanding of ML concepts and inferencing.
3. Demonstrated ability to translate technical insight into product strategy, including segmentation, positioning, differentiated requirements, and roadmap prioritization.
4. Proven effectiveness working with Engineering and Research teams in technically complex environments, driving alignment, trade-offs, and execution.
5. Basic programming proficiency and comfort working with technical artifacts.
6. Strong written and verbal communication skills, including the ability to produce clear technical narratives and enablement content for internal and external audiences.
7. High ownership and strong stakeholder management, with the ability to drive decisions, align cross-functional teams, and deliver outcomes.