Staff Machine Learning Engineer

📋 Job Overview

This role owns the decisioning system and underlying platform at MoonPay, a high-standards, high-velocity company building the operating system for value movement. The position involves building, shipping, and maintaining the entire machine learning lifecycle with a focus on fraud detection and prevention.

🏢 About MoonPay

MoonPay is for builders with something to prove. It's a high-standards, high-velocity, high-accountability company building the operating system for value movement. We move value: crypto, stablecoins, 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.

🎯 The Role

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.

✅ Key Responsibilities

• Turn vague problems into well-defined solutions and bring people with you.
• Set the technical bar through rigorous reviews, clear standards, and lasting engineering habits.
• 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.
• 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.
• 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.

📌 Required Qualifications

• 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 system to learn from its own decisions.
• Engineering craft. You write code other people are happy to inherit — tested, typed, and correct when events arrive twice, late, or out of order. Adding the next feature to something you built is fast and painless.
• Pipelines in production. You have owned feature or data pipelines end-to-end, including troubleshooting cases where offline and production metrics diverged and resolving the underlying discrepancies.
• Ambiguity and influence. You've taken a problem nobody had scoped and turned it into work that shipped, and raised the level of the engineers around you.

⭐ Desirable Experience

• Experience with fraud detection and prevention systems.
• Knowledge of machine learning model deployment and monitoring.
• Understanding of financial regulations and compliance requirements.
• Experience with cloud platforms and containerization technologies.

🎁 Benefits

Competitive salary and benefits package. The company offers a high-standards, high-velocity work environment with opportunities for growth and impact.

🛂 Visa & Eligibility

No relocation available. Candidates must be eligible to work in the UK.