Machine Learning Platform Engineer
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
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 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.
🧰 What you'll be working with...
- 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 DataHub
- 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 BigQuery 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).
🤩 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 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
🤔 This might NOT be the right fit if...
- Your background is predominantly SRE, DevOps or infrastructure operations
- You’re focus
Similar roles
Design & Tech
Related reads from TCHNX
The Quiet Revolution in Local-First Software
As major platforms face outages and data breaches, a new generation of developers is building applications that prioritise local data storage and peer-to-peer sync, challenging the cloud-first orthodoxy that's dominated tech for two decades.
The Quiet Revolution in Edge AI: Why Your Next Computer Might Not Need the Cloud
As neural processing units become standard in consumer devices, we're witnessing a fundamental shift in how AI applications work. Local processing is no longer a fallback; it's becoming the preferred architecture.
The Rise of AI-Assisted Code Generation 2: Are Developers Becoming Prompt Engineers?
As AI coding assistants reshape software development, the industry grapples with a fundamental question: is writing code giving way to writing prompts? We examine how London's tech scene is adapting to this seismic shift.