Senior Product Manager – AI Infrastructure
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Job Description
Product Manager owning AI inference product strategy for a semiconductor and accelerator company. Driving roadmap, competitive positioning, technical storytelling, and go-to-market enablement.
Responsibilities:
- Own the end-to-end lifecycle of the AI inference product direction
- Conduct market discovery and competitive intelligence
- Define requirements and execute the product roadmap
- Prepare the product for go-to-market
- Partner with Engineering and Research
- Collaborate with Marketing and Sales to translate technical differentiation into clear product narratives and measurable customer value
- Build understanding of the AI inference and LLM market, including buyer needs, adoption patterns, evaluation criteria, and emerging trends
- Identify high-value segments and use cases based on cost, performance, latency, throughput, energy, and deployment model
- Maintain an up-to-date view of competitors and alternatives, including chips, systems, cloud offerings, and inference stacks
- Translate competitive insights into positioning, requirements, and roadmap priorities
- Define product requirements, milestones, and success metrics with Engineering and Research
- Drive roadmap prioritization with clear tradeoffs and rationale
- Ensure execution remains connected to customer needs and measurable outcomes
- Partner with Marketing on messaging, launches, and content strategy
- Support Sales with pitch narratives, competitive battlecards, FAQs, case studies, and evaluation guidance
- Create technical content including blogs, whitepapers, solution briefs, product documentation, and presentations
- Articulate product differentiation to technical and business stakeholders
- Analyze benchmarks, workload characteristics, and performance/efficiency tradeoffs
- Build lightweight Python tools and scripts to validate assumptions and support product decisions
Requirements:
- Strong semiconductor and accelerator expertise, including familiarity with GPUs and TPUs
- Understanding of customer evaluation of hardware and system trade-offs, including performance, throughput, latency, deployability, and operational efficiency
- Strong machine learning depth, ideally supported by a university degree / PhD or significant experience in ML, AI systems, or a related area
- Solid understanding of ML concepts and inferencing
- Demonstrated ability to translate technical insight into product strategy, including segmentation, positioning, differentiated requirements, and roadmap prioritization
- Proven effectiveness working with Engineering and Research teams in technically complex environments, driving alignment, trade-offs, and execution
- Basic programming proficiency and comfort working with technical artifacts
- Strong written and verbal communication skills
- Ability to produce clear technical narratives and enablement content for internal and external audiences
- High ownership and strong stakeholder management
- Ability to drive decisions, align cross-functional teams, and deliver outcomes
Benefits:
- Full Time employment
- Remote work
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