• Scale up model inference and running predictions at scale on cutting-edge models • Work end to end to connect ML models to human interfaces (e.g. APIs, browsers, and applications) • Designing and implementing large-scale data and ML pipelines through a full end-to-end product development lifecycle • Collaborate with a cross-functional team of engineers, researchers, product managers, designers, and operations teammates to create cutting-edge products

• Build statistical models and production systems to balance advertiser performance with business goals. • Tune optimization parameters, measure internal competition, and model dynamic environments. • Design and run experiments to validate theories underpinning the mobile ad tech economy. • Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations. • Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM). • Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences. • Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Own the technical direction for pricing ML: Define what to build and how within the pricing engine, setting the strategy and roadmap for pricing machine learning as a core piece of tem's IP. • Build ML systems for price optimisation: Design and implement models that dynamically set prices, balancing the trade-off between signing probability, portfolio balance and margin maximisation. • Solve imbalance problems: Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment. • Bridge modelling and production: Own the modelling and data layer while working closely with software engineers and MLOps to ensure models are architected for production, contributing to system design decisions that affect performance and reliability. • Communicate pricing decisions clearly: Articulate model behaviour, assumptions, and trade-offs to other technical stakeholders so that pricing decisions are understood across the teams that depend on them.

• contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations • promote and role-model best practices of ML systems development • lead collaborations and align across PZN for A/B testing mid-term signals