Build and deploy machine learning models that improve recommendations, ranking, and personalization, driving measurable impact on user experience and engagement
Own problems end-to-end, from data exploration and feature engineering through to model training, evaluation, and production deployment
Develop and maintain scalable ML pipelines using tools such as Spark and Airflow to support reliable, high-quality model delivery
Apply modern ML frameworks (e.g. PyTorch or TensorFlow) to design, train, and optimise models in production environments
Contribute to experimentation frameworks, including A/B testing and offline evaluation, to iterate on model performance with an agile mindset
Collaborate cross-functionally with Product and Engineering, working with purpose to translate product questions into ML solutions
Take ownership of delivering high-quality solutions and see work through from insight to impact, balancing speed and rigor
Apply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment

Typically requires 5–8 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
Strong experience building and deploying machine learning models in production environments
Proficiency in Python and experience with at least one major ML framework (e.g. PyTorch, TensorFlow)
Experience working with data pipelines and distributed systems (e.g. Spark, Airflow) to support ML workflows
Familiarity with experimentation methodologies such as A/B testing and model evaluation techniques
Ability to collaborate effectively across functions, demonstrating strong ownership and a collaborative mindset
Demonstrates an agile mindset, adapting approaches based on data and evolving priorities while maintaining focus on outcomes
Growing AI fluency, with the ability to independently apply ML techniques and emerging tools (including LLMs) to solve problems responsibly

Senior Machine Learning Engineer

Key skills