Senior Machine Learning Engineer

About the Job

• Set the modelling and experimentation direction across the Central Product and Platforms funnel and personally lead the highest-stakes problems
• Architect systems that hold up at the scale of millions of bookings and a global user base, owning the hard trade-offs
• Make the call on contentious short-term technical questions, from metric definitions to model architecture, across the domain
• Resolve the measurement and modelling problems that block whole teams, and define the patterns they then reuse
• Influence Central Product and Platforms product and business strategy with a data-science perspective leaders trust
• Find where existing models, pipelines, or metrics quietly cost the business at scale, and drive the fix
• Mentor senior scientists and raise the technical bar across the domain through review and example

Job Requirements

• You have at least 10 years of relevant experience.
• You have the deepest technical command of Python and the ML/stats stack (scikit-learn, LightGBM, XGBoost, deep learning and optimisation where relevant)
• You write advanced SQL and architect the large-scale data and feature systems whole teams rely on
• You take the most ambiguous, domain-spanning problems and turn them into the bets the funnel is organised around
• You have authoritative command of search, recommendation, ranking, and pricing systems at marketplace scale
• You understand Central Product and Platforms economics deeply and anticipate the second- and third-order effects of a model change
• You design and adjudicate sophisticated experimentation and own the measurement standard others defer to
• You set technical direction across teams through influence, judgment, and decision authority on the issues in your remit
• You hold a degree in a quantitative field (Master’s or higher preferred) or equivalent, and influence senior cross-functional leaders
• You work confidently with cloud (GCP preferred) and production ML, and set the bar for AI-assisted ways of working
• You have a track record of models that delivered large, attributable business impact at marketplace scale
• You have designed an internal ML platform, feature store, or experimentation framework that changed how teams build
• You have worked on marketing or commercial measurement (MMM, MTA, propensity, CLV) that informed real spend or pricing decisions