Research Scientist, Recommendation Systems

Our team operates at the frontier of modern recommender systems. With a proven track record of innovating and deploying novel deep learning algorithms and systems at scale, we are currently focused on building the next-gen Large Recommendation Models by bridging the gap between LLMs and complex behavioral signals. Our research explores user & item tokenizations, continued pre-training, and advanced fine-tuning techniques to build recommendations-native foundation models. Our mission is to transform the landscape of recommendation systems using the most advanced AI technologies, delivering massive impact across Google’s flagship products.

As a Research Scientist, you will have the opportunity to build new paradigms using Large Language Models, harnessing the advanced content understanding, long-context, and reasoning capabilities. You will play a pivotal role in exploring how to integrate data from recommendation domains into foundation models, enabling new capabilities through data curation, Supervised Fine-Tuning (SFT), Reinforcement Learning (RL) training, and more.

• →Research and develop key technologies such as Semantic IDs, generative retrieval/ranking, large user models.
• →Build prototypes to demonstrate the "art of the possible" for recommendation systems using the newest AI advances.
• →Work closely with product teams to translate research breakthroughs into deployed solutions for flagship products, tackling real-world challenges at an industrial scale through new recipes.

We are seeking a Research Scientist who can drive new research ideas from conception and experimentation through to productionisation. In this rapidly shifting landscape, we regularly invent novel solutions to open-ended problems. You should be flexible, adaptable, and comfortable pivoting when ideas don’t work out.

In order to set you up for success as a  at Google DeepMind,  we look for the following skills and experience:

• PhD in Machine Learning, Computer Science, or a relevant field (or equivalent practical research experience).
• A proven track record of research excellence (e.g., publications at top-tier venues like NeurIPS, ICML, ICLR, or significant industry contributions), ranging from recent graduates to experienced researchers.
• Strong software engineering skills to complement your research background.

In addition, the following would be an advantage:

• Proven track record of building recommender / search systems and/or successfully deploying novel deep learning algorithms at industrial scale.
• Skilled in LLM post-training algorithms and infra, with proficiency in JAX.
• Strong communication skills with a demonstrated ability to drive cross-functional projects and collaborate effectively across organizational boundaries.