Product Manager – Lab

Role Overview

• Explore High-Risk, Technology-Driven Opportunities
• Identify opportunities emerging from new technologies, AI, data, platform capabilities, or infrastructure shifts
• Translate weak signals and technical possibilities into clear product hypotheses
• Explore ideas before there is a clear buyer, category, or demand signal
• Maintain an exploration backlog with risks, assumptions, and learning goals
• Design Experiments & Define Kill Criteria
• Frame experiments around the single riskiest assumption
• Define explicit kill criteria before building anything
• Choose the right fidelity: prototype, technical spike, wizard-of-oz, or live pilot
• Build just enough to learn — never more
• Run Fast Experiments & Pilots
• Execute scrappy prototypes, MVPs, and pilots with minimal scope
• Work closely with Engineering, Design, Data, and GTM during experiments
• Ruthlessly protect learning speed and avoid premature optimization
• Make Clear Investment Decisions
• Synthesize results into opinionated recommendations: Scale / Iterate / Kill
• Clearly communicate what was tested, what was learned, and what remains unknown
• Avoid zombie initiatives — every experiment must end with a decision
• Kill your own ideas quickly when evidence is weak
• Prepare Clean Escalation & Handoffs
• When an opportunity shows strong signal, prepare it for handoff with:
• Validated value and problem hypotheses
• Evidence from experiments or pilots
• Clear risks, assumptions, and success metrics
• A proposed ownership and scaling model
• Transfer ownership fully — the Lab does not run scaled products.
• Operate Transparently & Share Learnings
• Maintain a visible Lab portfolio: what's being explored, why, and what the signals say
• Publish decision memos and learning summaries
• Share failed experiments openly when the learning is clear
• Leverage AI to Accelerate Learning
• Use modern AI tools to speed up research, synthesis, prototyping, and experimentation
• Explore AI-enabled product ideas with a realistic lens: cost, latency, data, accuracy
• Distinguish hype from actual capability shifts
• Help others understand when AI meaningfully accelerates learning — and when it doesn't
• Launch High-Risk, High-Signal Initiatives
• Selectively launch bold initiatives even when short-term adoption is uncertain
• Treat launches as real product bets, not demos
• Use launches to test future categories, shape market perception, and signal technical leadership
• Be explicit about intent: learning, optionality, or external signaling

Requirements

• 5+ years in product roles with real 0→1 or exploratory ownership
• Personally owned multiple high-risk bets with explicit go / kill decisions
• Experience where learning speed mattered more than polish
• Comfort operating with real downside risk (time, opportunity cost, credibility)
• Strong hands-on experience using AI as a product-building and exploration tool
• Comfortable prototyping with LLMs, APIs, or modern tooling
• Able to scope AI experiments realistically (data, cost, latency, accuracy)
• Can judge feasibility without full engineering validation
• Curious beyond AI: automation, real-time data, voice, CV, infrastructure shifts.

Benefits

• 31 days of paid time off
• 100% paid telemedicine plan
• Home office setup assistance
• English learning courses
• Professional education budget
• Gym or swimming pool membership
• Co-working support
• Fully remote work