Staff Machine Learning Engineer in London

Staff Machine Learning Engineer in London

London Full-Time 63000 - 77000 £ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Lead the development of real-time decision systems for fraud detection and prevention.
  • Company: Join MoonPay, a high-velocity tech company transforming value movement in the crypto space.
  • Benefits: Enjoy competitive salary, equity options, flexible time off, and wellness perks.
  • Other info: Hybrid work model with opportunities for professional growth and mentorship.
  • Why this job: Make a real impact with cutting-edge AI technology in a fast-paced environment.
  • Qualifications: Experience in real-time serving, systems thinking, and engineering best practices required.

The predicted salary is between 63000 - 77000 £ per year.

About Moon Pay

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, 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.

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
  • 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

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 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, Big Query, Bigtable, Memorystore, Vertex AI, Kubernetes.

Benefits & Perks

  • Competitive salary package
  • Equity package: financial freedom starts with our employees, so all employees have ownership at Moon Pay
  • 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 remote 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, Chat GPT, 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
  • (Ireland) Cycle to Work scheme: tax-efficient bike, gear, and safety kit
  • (UK) EV Salary Sacrifice: lease an electric vehicle through pre-tax salary

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information.

These tools assist our recruitment team but do not replace human judgment.

Final hiring decisions are ultimately made by humans.

If you would like more information about how your data is processed, please contact us.

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Staff Machine Learning Engineer in London employer: MoonPay

MoonPay is an exceptional employer that fosters a dynamic and innovative work culture, particularly for those passionate about blockchain technology. Located in the United Kingdom, we offer competitive benefits, a strong focus on employee growth, and the opportunity to work on cutting-edge projects in the DeFi space. Join us to make a meaningful impact while collaborating with talented professionals in a fast-paced environment that values ownership and creativity.

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Contact Details:

MoonPay Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Staff Machine Learning Engineer in London

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like MoonPay!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Staff Machine Learning Engineer at MoonPay.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like MoonPay.

Apply Directly through Our Website

When you find a suitable opening like Staff Machine Learning Engineer at MoonPay, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Staff Machine Learning Engineer in London

Real-time Serving
High-Availability Services
Systems Thinking
Engineering Craft
Feature and Data Pipelines
Ambiguity Management
Decision Explainability

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at MoonPay, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at MoonPay. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at MoonPay

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at MoonPay!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.