Product Manager, Lab

Fundraise Up

2d•Remote

About The Position

Fundraise Up is at a stage where scaling the core is no longer enough. We need a systematic way to explore what's next — new capabilities, new categories, and new ways technology can reshape fundraising. The Lab exists to do exactly that. As a Product Manager, Lab, you will explore, validate, and de-risk bold, high-uncertainty product opportunities — many of which do not look like an obvious extension of what Fundraise Up does today. Your job is to reduce uncertainty, not to ship features. You will own ideas end-to-end: from early exploration and hypothesis framing, through fast experiments and pilots, to explicit investment decisions — scale, pivot, or kill. Most ideas should be killed early. A small number may graduate into New Markets or Core teams with strong evidence behind them. Success in this role is measured by learning speed and decision quality, not by output volume or adoption metrics

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
You don't need to be an engineer — but you should be able to: Ship a functional prototype quickly
Evaluate build vs buy vs partner decisions
Spot capability shifts and turn them into testable hypotheses

Responsibilities

Explore High-Risk, Technology-Driven Opportunities
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