Senior Machine Learning Engineer

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