About the job
We are the Recommendations Machine Learning (RecML) team in Core ML's Applied ML organization. Our mission is to accelerate product innovations through ML for recommendations and user modeling. We collaborate with various Alphabet product areas and partner with them to help accelerate product innovations through applied research in recommendations and user modeling. We also generalize successful innovations into standardized, maintainable, and production-grade solutions for use by other teams and products.
Responsibilities
Define and execute the applied research roadmap for the Large User Model ecosystem, balancing immediate customer needs with long-term technical evolution to scale foundational models across high-traffic surfaces.
Support initiatives with product leadership to translate complex business goals into technical model formulations, delivering step-function improvements in user engagement and business metrics while optimizing for latency and performance trade-offs.
Optimize model performance by researching and implementing adaptation techniques (transfer learning/domain adaptation) that balances high-quality output with strict inference latency requirements for production environments.
Drive architectural improvements by establishing a strategic feedback loop with pre-training teams, utilizing downstream performance analysis to influence data curation, model architecture, and novel evaluation metrics for engagement-specific needs.
Design and productionize fine-tuning pipelines that translate general-purpose state-of-the-art (SoTA) Foundation User Models into effective, domain-specific recommendation engines.
Qualifications
Minimum
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
5 years of experience with ML design (e.g., model deployment, model evaluation, data processing, debugging, fine-tuning).
Experience with Transformer-based models (e.g., BERT, T5, GPT, ViT), including attention mechanisms and architecture variations.
Preferred
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
8 years of experience with data structures/algorithms.
3 years of experience in a technical leadership role leading project teams and setting technical direction.
3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
Experience of publishing in venues or contributing to open-source projects related to RecSys, transfer learning, NLP/CV, or multimodal systems.
Understanding of modern recommendation architectures (e.g., two-tower models, sequential user modeling) and how to integrate Large Foundation Models into existing ranking/retrieval stacks.