Principal AI Research Scientist, Research Director - AI Scaling

About the Role As a Principal Research Scientist – AI Scaling, you will lead a world‑class team of researchers and engineers to advance state-of-the-art large-scale machine learning. You will define and execute a research roadmap that advances the Databricks AI platform and delivers tangible improvements to how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders to bring cutting-edge methods into production. What You'll Do

• Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with emphasis on LLM scaling, efficiency, and systems performance.
• Define the scaling research roadmap in alignment with Databricks’ strategic objectives, prioritizing advances in foundation model efficiency and large-scale training and inference.
• Drive algorithmic innovations for large-scale neural network training and inference, including novel optimizers, low-precision techniques, and model adaptation methods, and guide your team in rigorous empirical validation against state-of-the-art approaches.
• Optimize end-to-end ML systems for distributed training and RL, memory efficiency, and compute efficiency through close collaboration with core systems and platform teams, ensuring that research ideas translate into performant, reliable infrastructure.
• Partner with product and engineering to translate research breakthroughs, especially around scaling and efficiency, into production. What We're Looking For
• Experience leading a multidisciplinary research team of researchers and engineers, with a track record of successful AI scaling initiatives.
• Ability to define and execute a research roadmap aligned with strategic objectives and translate research into product impact.
• Proven track record of driving algorithmic innovations for large-scale neural network training and inference, including optimizers, low-precision techniques, and model adaptation.
• Strong experience in optimizing distributed training, RL, memory and compute efficiency, with ability to collaborate with core systems and platform teams.
• Demonstrated ability to translate research breakthroughs into production, collaborating with product and engineering teams.