Monitor the evolving landscape of recommendation systems, actively prototyping and benchmarking emerging modeling techniques to keep our infrastructure cutting-edge and efficient.

Enable next-generation model architectures and training procedures.

Scale experimentation capacity under our resource envelope.

Reduce complexity and fragmentation in the ML training and serving ecosystem by providing standardized, composable, and reusable solutions.

Reduce experimenter toil through introduction of automation frameworks for training, evaluation, and model serving.

Bachelor’s degree or equivalent practical experience.

8 years of experience in software development.

5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

3 years of building large-scale recommendation systems, Machine Learning (ML), ranking, or personalization.