(Senior)Product Manager - AI Infrastructure

Role Overview

We are hiring a Product Manager or Senior Product Manager to own the end-to-end lifecycle of our AI inference product direction. You will partner closely with Engineering and Research while collaborating 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, emerging trends)
• Identify high-value segments and use cases where we can win (cost, performance, latency, throughput, energy, deployment model)

Competitive & Ecosystem Analysis

• Maintain an up-to-date view of competitors and alternatives (chips, systems, cloud offerings, 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, evaluation guidance

Technical Storytelling & Content

• Create crisp, credible technical content (blogs, whitepapers, solution briefs, product docs, 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

• Strong semiconductor and accelerator expertise: familiarity with GPUs and TPUs, understanding of how customers evaluate hardware and system tradeoffs (performance, throughput, latency, deployability, operational efficiency)
• Strong machine learning depth: university degree/PhD or significant experience in ML, AI systems, or related area; solid understanding of ML concepts and inferencing
• Technical-to-strategy translation: demonstrated ability to translate technical insight into product strategy (segmentation, positioning, differentiated requirements, roadmap prioritization)
• Cross-functional leadership: proven effectiveness working with Engineering and Research teams in technically complex environments, driving alignment, tradeoffs, and execution
• Technical proficiency: basic programming proficiency and comfort working with technical artifacts
• Communication: strong written and verbal communication skills, including ability to produce clear technical narratives and enablement content
• Ownership & stakeholder management: high ownership and strong stakeholder management, ability to drive decisions, align cross-functional teams, and deliver outcomes