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
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Market research & strategy
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build a sharp understanding of the AI inference and LLM market (buyer needs, adoption patterns, evaluation criteria, and emerging trends)
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identify high-value segments and use cases where we can win (cost, performance, latency, throughput, energy, deployment model, etc.)
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Competitive & ecosystem analysis
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maintain an up-to-date view of competitors and alternatives (chips, systems, cloud offerings, and inference stacks)
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translate competitive insights into positioning, requirements, and roadmap priorities
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Requirements & roadmap ownership
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work with Engineering and Research to define product requirements, milestones, and success metrics
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drive roadmap prioritization with clear tradeoffs and rationale
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ensure execution stays connected to customer needs and measurable outcomes
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GTM + sales enablement
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partner with Marketing on messaging, launches, and content strategy
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support Sales with enablement assets: pitch narratives, competitive battlecards, FAQs, case studies, and evaluation guidance
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Technical storytelling & content
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create crisp, credible technical content (blogs, whitepapers, solution briefs, product docs, and presentations)
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articulate “why we win” clearly to technical and business stakeholders
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Hands-on analysis
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use data to drive decisions: analyze benchmarks, workload characteristics, and performance/efficiency tradeoffs
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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:
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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.
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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.
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Demonstrated ability to translate technical insight into product strategy, including segmentation, positioning, differentiated requirements, and roadmap prioritization.
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Proven effectiveness working with Engineering and Research teams in technically complex environments, driving alignment, trade-offs, and execution.
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Basic programming proficiency and comfort working with technical artifacts.
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Strong written and verbal communication skills, including the ability to produce clear technical narratives and enablement content for internal and external audiences.
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High ownership and strong stakeholder management, with the ability to drive decisions, align cross-functional teams, and deliver outcomes.