Highlights
- Location: Serbia, Remote
- Stock options
- Languages: Fluent in English and Russian.
About Fundraise Up
Fundraise Up is a global fundraising platform built to help nonprofits raise more by delivering fast, seamless, and high-converting donation experiences.
Our technology powers tens of millions of dollars in donations every month for organizations across North America, Europe, the UK, and Australia — including some of the world's most impactful nonprofits. We are known for product quality, performance, and a deep respect for both donors and nonprofit operators.
As we scale our core platform, we are also deliberately investing in high-risk, high-upside product bets that could define the future of digital fundraising. That's where the Lab comes in.
About the Role
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
What You'll Do
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
Must-Have Experience
This role is closer to a Senior / Principal IC PM than to a classic team PM. We expect strong individual judgment and minimal need for process scaffolding.
- 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)
This experience may come from:
- Founder or co-founder roles
- Early-stage startups
- Internal Labs / innovation teams
- New market or platform bets inside larger companies
AI & Technical Judgment (Critical)
- 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
Why Work With Us
- Real ownership over meaningful, high-impact bets
- Leadership that values clear thinking over activity
- Permission — and expectation — to kill bad ideas early
- A product culture that values judgment, taste, and learning
- Access to real customers, real data, and real constraints
- Long-term focus with equity participation
Benefits
Our compensation package includes comprehensive benefits and perks, meaningful equity, and a competitive salary:
- 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
