Senior Machine Learning Engineer

Job Brief

You might be wondering what Machine Learning would you support if you look at the Moneybox app today - and you wouldn’t be wrong. Our footprint in ML is fairly limited - but Moneybox has embarked on an exciting journey to improve the experience and outcomes of customers through an increased level of personalisation - which needs to be powered by a range of models, including but not limited to recommendations ML and GenAI, across a variety of content, touchpoints and customer experiences. 

As senior ML engineer in Moneybox you would be starting at a point where we’re building out a lot of the foundational architecture and modelling capability to power our next phase of growth. You would work with the data scientists to take their prototypes into production and make decisions on deployment patterns to serve the customers in a cost-effective manner while supporting the systems of our wider cloud applications teams. 

We are a fairly lean team - and in the same way that our data scientists support their engineering colleagues when needed, we’d expect a minority of your time to be spent in support of the elements of data science workflows as well. Never at the expense of work-life balance though. 

We are currently setup on Databricks@Azure and we’d envision a lot of the infrastructure we’ll build to utilise ML Flow, but outside of that - we’re pretty relaxed of the chosen patterns and frameworks you might pursue. 

What you'll do

You will be solely responsible for choosing and governing an efficient deployment and training of models Taking prototypes from data scientists and productionising them Optimise the training and deployment along the cost / performance boundary Where learning rates are slow - work with data scientists to develop solutions for transfer learning from parallel use cases Monitor the deployed training and inference architectures and ensure ongoing performance Work with the decision science team and Head of Decision Science to input into choices on objective functions, content strategy and wider data strategy to ensure good long-term ML outcomes

Who you are

Have experience in running productionised ML systems at scale of millions of users Enjoy optimising things and have an optimisation mindset when considering your problem solves Are a systems thinker and enjoy figuring out a scalable solution that can fit an emerging system Thrive in a fast-paced startup environment Eager to learn new things and challenge your existing frameworks Are not scared of ambiguity (even though this will be the least ambiguous job position in our team) You enjoy being a jack of all trades - we recognize that you can only be a master of some

Experience and skills - essential

2 years of industry experience owning ML models in production environments facing customers, preferably in a consumer business At least 1 year of experience owning end-to-end build of a productionised ML system Experience in setting up model-serving API’s to interface with other engineering organisations Knowledge of applied machine learning, model tuning and model evaluation Experience with machine learning optimization, with demonstrated experience of navigating the cost/performance optimization Experience with using feature stores to consolidate signal collection, research and training Demonstrated experience of using good governance to track model performance, model versioning and model training

Experience and skills – not essential for the role, but will be counted as a plus

Experience in deploying using any of: Databricks, Azure or ML Flow would be seen as a plus Experience with putting open-source LLM’s into production in customer-facing commercial environments Demonstrated experience of setting up ML systems end-to-end, including governance and choice of architecture