Lead the design and delivery of end-to-end ML systems powering recommendations, ranking, and personalization, driving measurable improvements in user outcomes
Architect and optimise ML pipelines, integrating data processing, model training, evaluation, and deployment into scalable systems (e.g. Spark, Airflow)
Build and deploy advanced models using modern frameworks (e.g. PyTorch), ensuring robustness in high-scale production environments
Develop and apply LLM-based capabilities, including prompt design and fine-tuning approaches for production use cases
Drive experimentation strategy, including A/B testing and offline evaluation, to continuously improve performance and inform product decisions
Partner cross-functionally with Product, Engineering, and Data teams, collaborating with purpose to translate business challenges into ML solutions
Mentor and support other engineers, fostering a culture of Curiosity, ownership, and continuous improvement

Typically requires 7–10 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
Strong expertise in machine learning, with a track record of delivering end-to-end systems in production
Proficiency in Python and ML frameworks (e.g. PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking, or NLP
Experience designing and scaling ML pipelines and data systems (e.g. Spark, Airflow, or similar technologies)
Hands-on experience with LLMs, including prompting, evaluation, and production integration
Demonstrated ability to take ownership of ambiguous problems and deliver impactful outcomes
Strong collaboration skills, working cross-functionally and taking ownership of shared goals
Solid AI fluency, with the ability to guide best practices in model development and responsible AI usage within a team.

Lead Machine Learning Engineer

Key skills