Principal AI Research Scientist, Research Director - AI ScalingActive$270K

Databricks
Apply now

The opportunity

As a Principal Research Scientist – AI Scaling, you will lead a team of world‑class researchers and engineers to advance the state of the art in large‑scale machine learning, focusing on post-training, RL and inference efficiency, optimization, and scaling. You will define and…

What you'll do

  • Lead and grow a multidisciplinary research team focused on foundational and: applied AI problems, with a particular 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 customer‑impacting capabilities in the Databricks AI platform.

  • Foster a culture of scientific excellence and openness, including: high‑quality research practices, reproducible experimentation, and effective internal knowledge sharing across Databricks AI.

What they're looking for

  • Represent Databricks AI research externally through top‑tier publications,: conference talks, and collaborations with academia and the open‑source community, with a focus on optimization and efficiency for large‑scale models.
  • Mentor and develop talent, providing both technical guidance (research: agendas, experimentation, implementation) and career development support for research scientists and engineers.
  • Define and lead independent research programs on foundation model: efficiency, covering topics such as optimizer design, low‑precision training/inference, scalable model architectures, and efficient adaptation methods.
  • Oversee the design and execution of large‑scale experiments, including: benchmarking against state‑of‑the‑art methods and evaluating trade‑offs in quality, latency, throughput, and cost.