About the role
The core responsibilities for the job include the following:
ML Development and Deployment:
- Design and develop cutting-edge models for predicting ad outcomes, bidding decisions, budget pacing, and SKU/ad selection.
- Build intelligent automation systems for campaign management that adapt in real-time to advertiser and platform feedback.
- Develop generative models to optimise ad creatives and improve user engagement across formats.
System Architecture and Optimisation:
- Own the architecture of core components like model pipelines, simulation frameworks, and personalisation layers within the ad platform.
- Drive research into long-term value modelling, multi-touch attribution, and campaign lifecycle optimisation.
Cross-Functional Impact:
- Collaborate with Product and Engineering to design features that are ML-first and measurable.
- Act as the primary technical stakeholder for experimentation, metrics design, and long-term roadmap planning.
Thought Leadership and Innovation:
- Explore and prototype novel approaches using reinforcement learning, RLHF, contrastive learning, or LLMs for advertiser assistance or campaign agents.
- Publish internal whitepapers or contribute to external publications and IP generation when applicable.
Requirements:
- Strong understanding of learning-to-rank, bandits and exploration strategies; representation learning / embeddings; auction systems and ad marketplaces; multi-objective optimisation; real-time inference; latency-sensitive model serving; and model monitoring in production environments.
- Fluency in Python and ML frameworks like PyTorch, TensorFlow, and XGBoost and libraries like LightGBM and Scikit-learn.
- Expertise in large-scale data processing with PySpark, SQL, etc.
- Experience with a strong command of A/B testing design, incrementality measurement, and online experimentation in ad tech systems.