Machine Learning Platform Engineer

About Machine Learning Platform Engineering at Monzo

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

The Machine Learning Platform team builds the systems that help teams across Monzo train, evaluate, deploy and serve ML 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.

• Go for backend services, platform APIs, and production systems
• Python for libraries, workflows, and tooling used by our ML engineers and scientists
• Feature platforms and data workflows using Chronon, Feast, and Data Hub
• Model training pipelines and experiment tracking using Vertex AI and Comet
• AI observability, evaluation, and tracing using Langfuse and Bifrost
• AWS for real-time serving and online inference, GCP for batch compute and our Big Query data warehouse.

(Please note direct experience with all of them is helpful but not required and our interview process can be completed in any language).

• 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 enjoy ambiguity and want to shape a platform as it grows
• You have 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

• Your background is predominantly SRE, Dev Ops 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 Engineers of varying levels at the moment. You can read more in our Engineering Progression Framework – we will interview you across the entire framework, so if you are not sure what level you are aiming for please chat to your recruiters!

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…