Start Date
Immediate
Expiry Date
08 Nov, 25
Salary
145000.0
Posted On
09 Aug, 25
Experience
0 year(s) or above
Remote Job
Yes
Telecommute
Yes
Sponsor Visa
No
Skills
Good communication skills
Industry
Electrical/Electronic Manufacturing
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 ❤️
Hear from our UK team about what it’s like working at Monzo ✨
London or Remote (UK) | Base salary for this role is £110,500 - £145,000 (depending on experience) + stock options + Benefits
YOU SHOULD APPLY IF:
While this is not a role which requires hands-on-coding, we are looking for an experienced manager who has a strong technical and delivery background and has worked as a software engineer and has worked closely, in or with, ML Platforms in past roles.
We recognise that engineering managers lead in different ways, we’re looking for someone who:
We consider your application across all open EM roles at Monzo independent of which role you’ve applied for, so if you’ve recently applied to a similar role at Monzo and have been unsuccessful, please wait 6 months before applying again.
We are seeking an experienced leader in Machine Learning Platform Engineering to guide a team in developing a robust, scalable, and high-performance ML platform. This platform will cover the entire ML development lifecycle, including model experimentation, training, feature engineering, and serving at scale.
You will work closely with ML and Data, as well as the wider engineering discipline, to equip our teams with the tools and frameworks needed to effectively build, deploy, and scale ML-driven solutions.