ML/AI Platform Engineer at Monzo Bank

Cardiff, Wales, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

20 Sep, 26

Salary

110000.0

Posted On

22 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Go, Python, Kubernetes, AWS, GCP, Terraform, Envoy Proxy, Distributed Systems, ML Pipelines, Model Serving, Feature Stores, LLM Infrastructure, Backend Engineering, System Design, API Development, Experiment Tracking

Industry

Financial Services

Description

🚀 We’re on a mission to make money work for everyone. We’re waving goodbye to the complicated and confusing ways of traditional banking. After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts, accounts for 16-17 year olds, a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save, invest and combine their pensions with us. With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers! We’re not about selling products - we want to solve problems and change lives through Monzo ❤️ 📍London / UK Remote | £85,000 - £110,000 + Incentive Awards tied to your performance + Benefits About our Engineering Teams: We have around 600 engineers out of roughly 5,000 people in total - and we have big ambitions. There are many interesting challenges ahead, and we're happy for people to move between teams or to specialise, whatever you prefer. As an engineer here you'd be able to work directly with anyone across the company, and we run regular knowledge-sharing sessions so you’ll learn heaps about everything from how banks work to effective communication. We contribute to open source software as much as possible. Our blog is a good place to learn even more about what we do. About Machine Learning Platform Engineering at Monzo: The Machine Learning Platform team builds the systems that help teams across Monzo train, evaluate, deploy and serve machine learning models and AI features safely and reliably. We work on backend services, Python libraries, model lifecycle tooling, evaluation workflows and low-latency serving systems. Our users are internal ML engineers, scientists and product teams building with ML and LLMs. The work matters because machine learning powers many important decisions and experiences at Monzo, from fraud checks and credit decisions to customer operations. We help teams move faster while keeping production systems reliable, observable and safe. This is a platform engineering role in the ML and AI space. We’re looking for someone who combines strong software engineering foundations with ML or AI context, and who enjoys building tools and systems for other engineers. How we work 💻 Locations & Flexible Working: Our main tech hub is in London, but our engineers live everywhere in the UK— from Brighton to the Western Isles. We value meeting in person but there’s no pressure to come into the office, even if you're nearby. We believe you'll do your best work if you are where you want to be. If you live outside of London and we ask you to come into the office, Monzo will support you with the costs. ✨ Our offices are naturally social, especially Tuesdays, Wednesdays and Thursdays, which happen to line up with our twice-weekly Monzo lunches & treat Thursdays 🍽️. Teams also schedule time together often for work and play – in or around the office, or online. Set up a work schedule that delivers impact and fits your life: At Monzo, we value connections, flexibility, and wellbeing. We keep our meetings during core hours to stay connected and believe in maintaining work/life balance. You’ll be empowered to manage your work in a way that suits you and your team, giving you the freedom for children drop-offs and pick-ups, walking your dog or adventurous cat, avoiding peak commuting times or gym slots, appointments, or supporting your family in an emergency 🕣✨ If you prefer to work part-time, we'll make this happen whenever we can - whether this is to help you meet other commitments or strike a great work-life balance. What you'll be working with: We use a mix of backend, ML and infrastructure technologies. Please note direct experience with all of them is helpful but not required and our interview process can be completed in any language. Go for backend services, platform APIs and production systems Python for libraries, workflows and tooling used by ML engineers and scientists ML and AI platform systems such as model serving, evaluation, feature engineering, experiment workflows and LLM infrastructure Kubernetes, AWS, GCP, Terraform and Envoy Proxy to run reliable systems in production 🤩We’d love to hear from you if... You combine solid backend engineering with real ML or AI platform experience (ML pipelines, feature stores, model serving, experiment tracking or LLM tooling) You’ve designed and operated distributed systems that handle scale, concurrency and failure You think like a platform engineer, focused on developer experience and removing friction for internal teams You’re happy working across both Go and Python You’ve worked with AWS or GCP, and with Kubernetes You enjoy ambiguity and want to shape a platform as it grows You have some experience with strongly typed languages, writing and working on backend software You’re curious about how systems behave in production, including reliability, latency, quality, safety and operational risk 🤔This might NOT be the right fit if... Your background is predominantly DevOps, SRE or infrastructure operations You’re focused on data science or modelling You’ve shipped AI product features but haven’t worked on the platform side (serving, evaluation, model lifecycle) We're on the look out for L40 Engineers at the moment, you can read more in our Engineering Progression Framework - we will interview you across the whole frame work, so if you are not sure what level you are aiming for please chat to your recruiters! The Interview Process: Our interview process involves three main stages: Initial Call Take home task or pair coding exercise Final interview: including a system design and a behavioural interview Our average process takes around 4 weeks but we will always work around your availability. One of our engineers has written a detailed blog on their experience through this process, for extra details, hints and tips please see here. What’s in it for you: 💰 £85,000 - £110,000 + Incentive Awards tied to your performance ✈️We can help you relocate to the UK ✅We can sponsor visas. 📍This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). ⏰We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team. 📚Learning budget of £1,000 a year for books, training courses and conferences ➕And much more, see our full list of benefits here #LI-NB2 #LI-Remote Equal opportunities for everyone Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog, 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report. We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status. If you have a preferred name, please use it to apply. We don't need full or birth names at application stage 😊

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Responsibilities

Build and maintain systems that enable internal teams to train, deploy, and serve machine learning models and AI features safely. Develop backend services, Python libraries, and low-latency serving systems to support fraud checks and credit decisions.