Staff Machine Learning Engineer

MoonPay; Deutschland; Hybrid; EUR 105.000 - 163.000; Vollzeit

Benefits dieser Stelle

Equity package

Pension

Private healthcare

Hybrid schedule

Lunch credit

Remote working allowance

Annual training budget

Zusammenfassung

MoonPay in London is seeking a Staff Machine Learning Engineer to own the decisioning system for real-time transaction scoring. You will build and maintain the full ML lifecycle, from data pipelines to deployed models, focused on fraud detection and broader ML capabilities across MoonPay.

You will lead through ambiguity, set engineering standards, and scale the platform to handle increasing volume and model complexity.

Qualifikationen

• Real-time serving on high-availability services with strict latency budgets.
• Holistic architecture thinking with graceful degradation.
• Clean, well-tested, and maintainable code.
• End-to-end feature or data pipelines ownership.
• Proven ability to turn vague problems into shipped solutions.

Aufgaben

• Own the end-to-end ML lifecycle from training to retirement.
• Improve the decisioning platform for fraud detection and scale it.
• Collaborate across teams to ship safe, real-time models.
• Design replay/shadow/staged-rollout tooling.

Kenntnisse

Real-time serving

Systems thinking

Engineering craft

Pipelines in production

Ambiguity and influence

Tools

GCP

Kubernetes

Vertex AI

Jobbeschreibung

About MoonPay

MoonPay 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, 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.

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.

Locations Supported

• London, UK

Relocation available: No

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

About the Opportunity

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.

As a Staff Machine Learning Engineer, you will hold a hands‑on technical position. You will be part of a team that builds, ships, and maintains the entire machine learning lifecycle.

Our main focus is fraud detection and prevention, an adversarial domain where opponents constantly adapt and feedback arrives in the form of financial impact. Alongside, this we build broader capabilities to enable machine learning across Moonpay.

Lead through ambiguity

• 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.

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.

Must-have experience and skills

• 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 while doing it.

Nice-to-have experience

• Decision explainability. You've built systems where the reason for a decision mattered as much as the decision: audit trails, per-layer attribution, llm-driven analyses, or defending a model's behaviour to a non-technical audience.
• Anomaly detection. You have developed systems to detect novel attack patterns and emerging abuse without existing labels, identifying suspicious behavior relative to historical baselines.
• Familiarity with our stack: GCP, BigQuery, Bigtable, Memorystore, Vertex AI, Kubernetes.

Benefits & Perks

• Competitive salary package
• Equity package: financial freedom starts with our employees, so all employees have ownership at MoonPay
• Pay-for-performance equity bonus: those who drive outsized outcomes receive outsized rewards
• Moonshot award: we honor exceptional impact. 10 employees twice a year, each earning a $250,000 equity grant
• Pension: employer contributions from day one
• Employee referral program: refer great people, earn 10K in USDC
• Flexible Time Off: choose when to work and when to switch off
• Birthday leave: take the day off to celebrate you
• Enhanced parental leave: more time with family, no second thought
• Hybrid working schedule: work fully remotely or from your nearest Moonbase
• Commuter benefits: public transport to and from the office
• Private healthcare benefits: to protect you and your loved ones
• Wellhub wellness membership: access to gyms, studios, classes, and wellness apps in one membership
• Unlimited enterprise access to the latest AI tools: Claude, ChatGPT, Gemini and whatever's next
• Lunch credit: meals covered on the days you're in the office
• Home office setup allowance: build the home office of your dreams
• Remote working allowance: those working fully remotely get a little extra for utilities
• Monthly product budget and zero-fee crypto transactions
• $1,000 Annual training budget: we support your learning journey
• High Potential Program: structured development, mentorship, and stretch opportunities
• Regular remote company offsites: high-impact in-person sessions and hackathons
• Cycle to Work scheme: tax-efficient bike, gear, and safety kit
• EV Salary Sacrifice: lease an electric vehicle through pre-tax salary