Machine Learning Engineer

Moon Pay is for builders with something to prove.

This isn't a "work on cool crypto stuff" company. It's a high-standards, high-velocity, high-accountability company building the operating system for value movement. If the internet moves information, we move value: crypto, stable coins, tokenized assets, and whatever comes next. Four offerings make that real: fund, tokenize, trade, and spend. 30M+ customers and 500+ ecosystem partners run on us. Licensed in the U.S. Regulated across the UK, EU, Canada, and Australia.

AI is the default operating mode here. It's woven into every role, and we expect you to use it daily. It handles the manual work so you can deliver on what actually matters.

You'll thrive here if outcomes excite you more than process, if impact motivates you more than titles, and if you want hard problems, real ownership, and teammates who love winning, building, and doing it together.

The bar is high. The pace is real. We're building for what's next, for humans and agents.

Recent recognition:[Forbes' America's Best Startup Employers 2026 .]( 2nd in Crypto Services on [Fortune's inaugural Crypto 100]([The Sunday Times Best Places to Work]( two years running.

Research has shown that women are less likely than men to apply for this role if they do not have experience in 100% of these areas. Please know that this list is indicative, and that we would still love to hear from you even if you feel that you are only a 75% match. Skills can be learned, diversity cannot.

London, UK

Relocation available:
No

Hybrid: our teams meets in the office ~1-2 days a week

Every transaction we process requires a real-time decision. Declining a legitimate transaction leaves a customer stuck at the point of purchase, while approving a fraudulent one carries a direct cost. This role owns the decisioning system and underlying platform. From the serving path and feature infrastructure to the underlying models and the machinery required to make safe, live updates. You will continuously improve the platform and our day to day workflows, rather than treating these as secondary projects.

Turn vague problems into well-defined solutions and bring people with you.

Build and scale the platform

Develop feature infrastructure across batch, near-real-time, and in-request paths, managing specific freshness budgets for each.

Maintain alignment between training and serving to ensure models behave in production exactly as they did offline.

Integrate feedback loops to capture every decision and its outcome, including blocked transactions where results are counterfactual.

Scale the platform as volume and model complexity grow, ensuring operational load remains manageable.

Decide in real time

Own the services that score transactions in-flight, inside a hard latency budget

Design the degraded paths: what we answer when the model can't, and who agreed that policy

Ship safely, continuously

Mature the replay, shadow and staged-rollout tooling until changing a live model is routine and reversible

Own models across their lifecycle, from training through to retirement, and catch decay long before losses confirm it.

• Real-time serving.
• You have built and operated high-availability services that execute within strict latency budgets on critical paths, and you’ve designed robust fallback mechanisms.
• Systems thinking.
• You view the architecture holistically: identifying failure points, managing graceful degradation, and ensuring the system remains responsive even when dependencies fail. You build the feedback loops that allow a…