MoonPay
Staff Machine Learning Engineer

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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 π
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.
Reasons to use Rodeo
Iβm in my final year doing Economics and I donβt know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer β it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) donβt need a masters. Letβs look at the ones youβd be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet β that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant β 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
Youβve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon β deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme β client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right β and where to focus when applying.
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.
About You
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 youβve 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 β 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.


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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 π‘
- π° Competitive salary package
- π€ Equity package: financial freedom starts with our employees, so all employees have ownership at MoonPay
- π Pay-for-performance equity bonus: those who drive outsized outcomes receive outsized rewards
- π Moonshot award: we honor exceptional impact. 10 employees twice a year, each earning a $250,000 equity grant
- π Pension: employer contributions from day one
- π Employee referral program: refer great people, earn 10K in USDC
- π Flexible Time Off: choose when to work and when to switch off
- π Birthday leave: take the day off to celebrate you
- πΌ Enhanced parental leave: more time with family, no second thought
- π Hybrid working schedule: work fully remotely or from your nearest Moonbase
- π Commuter benefits: public transport to and from the office
- π©Ί Private healthcare benefits: to protect you and your loved ones
- π§ Wellhub wellness membership: access to gyms, studios, classes, and wellness apps in one membership
- π€ Unlimited enterprise access to the latest AI tools: Claude, ChatGPT, Gemini and whatever's next
- π± Lunch credit: meals covered on the days you're in the office
- πͺ Home office setup allowance: build the home office of your dreams
- π Remote working allowance: those working fully remotely get a little extra for utilities
- π Monthly product budget and zero-fee crypto transactions
- π $1,000 Annual training budget: we support your learning journey
- π― High Potential Program: structured development, mentorship, and stretch opportunities
- βοΈ Regular remote company offsites: high-impact in-person sessions and hackathons
- π² (Ireland) Cycle to Work scheme: tax-efficient bike, gear, and safety kit
- π (UK) EV Salary Sacrifice: lease an electric vehicle through pre-tax salary
βIt took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what Iβve been looking for.β
Jessica, London
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