Staff Machine Learning Engineer
Moonpay is hiring a Staff Machine Learning Engineer in London, United Kingdom. Level rates it ; you can apply on Moonpay.
Level's read on this job
- On Level's scale, this machine learning engineer role is Little AI ●○○○: aI is not part of the work.
- Tools and skills the posting cites include ChatGPT, Claude and Gemini.
- Level classifies it as a lead role.
- Level lists 21 other open roles at Moonpay.
AI in this role
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 🌍
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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
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Turn vague problems into well-defined solutions and bring people with you.
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Set the technical bar through rigorous reviews, clear standards, and lasting engineering habits.
Build and scale the platform
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Develop feature infrastructure across batch, near-real-time, and in-request paths, managing specific freshness budgets for each.
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Maintain alignment between training and serving to ensure models behave in production exactly as they did offline.
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Integrate feedback loops to capture every decision and its outcome, including blocked transactions where results are counterfactual.
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Scale the platform as volume and model complexity grow, ensuring operational load remains manageable.
Decide in real time
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Own the services that score transactions in-flight, inside a hard latency budget
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Design the degraded paths: what we answer when the model can't, and who agreed that policy
Ship safely, continuously
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Mature the replay, shadow and staged-rollout tooling until changing a live model is routine and reversible
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Own models across their lifecycle, from training through to retirement, and catch decay long before losses confirm it
About You
Must-have experience and skills
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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
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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.
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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.
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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.
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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
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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.
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Anomaly detection. You have developed systems to detect novel attack patterns and emerging abuse without existing labels, identifying suspicious behavior relative to historical baselines.
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Familiarity with our stack: GCP, BigQuery, Bigtable, Memorystore, Vertex AI, Kubernetes.
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.
Benefits & Perks 💡
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💰 Competitive salary package
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🤝 Equity package: financial freedom starts with our employees, so all employees have ownership at MoonPay
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📈 Pay-for-performance equity bonus: those who drive outsized outcomes receive outsized rewards
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🚀 Moonshot award: we honor exceptional impact. 20 employees a year, each earning a $250,000 equity grant
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📊Pension: employer contributions from day one
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🎁Employee referral program: refer great people, earn 10K in USDC
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🏝 Flexible Time Off: choose when to work and when to switch off
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🔋Recharge Period: end-of-year company-wide time off to rest and reset
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🎂 Birthday leave: take the day off to celebrate you
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🍼 Enhanced parental leave: more time with family, no second thought
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🌍 Hybrid working schedule: work fully remotely or from your nearest Moonbase
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🚆 Commuter benefits: public transport to and from the office
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🩺 Private healthcare benefits: to protect you and your loved ones
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🧘 Wellhub wellness membership: access to gyms, studios, classes, and wellness apps in one membership
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🤖 Enterprise access to the latest AI tools: Claude, ChatGPT, Gemini and whatever's next
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🍱 Lunch credit: meals covered on the days you're in the office
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🪑 Home office setup allowance: build the home office of your dreams
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👛 Remote working allowance: those working fully remotely get a little extra for utilities
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🌕 Monthly product budget and zero-fee crypto transactions
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📚 $1,000 Annual training budget: we support your learning journey
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🎯 High Potential Program: structured development, mentorship, and stretch opportunities
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✈️ Regular remote company offsites: high-impact in-person sessions and hackathons
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🚲 (Ireland) Cycle to Work scheme: tax-efficient bike, gear, and safety kit
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🔌 (UK) EV Salary Sacrifice: lease an electric vehicle through pre-tax salary
How we rate this
Staff Machine Learning Engineer at Moonpay rates 8 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- Typical machine learning engineer job
- $200k
This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs
Questions you could be asked
- What's a project where you used ChatGPT hands-on?
- Walk me through how you've used Claude in your day-to-day work.
- What are the limits of Gemini that you've run into, and how did you work around them?
- What's a project where you used Vertex AI hands-on?
Adapt your resume
- List these exact terms on your resume: ChatGPT, Claude, Gemini, and Vertex AI. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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