Sr. Staff AI Research TLM - AI Systems

Responsibilities

• 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 rigorous empirical validation.
• Optimize end-to-end ML systems for distributed training and RL, memory efficiency, and compute efficiency through collaboration with core systems and platform teams.
• Partner with product and engineering to translate research breakthroughs into customer-impacting capabilities in the Databricks AI platform.
• Foster a culture of scientific excellence, reproducible experimentation, and internal knowledge sharing.
• Represent Databricks AI research externally through top-tier publications, conference talks, and collaborations with academia and open-source community.
• Mentor and develop talent, providing technical guidance and career development support.

Key Requirements

• Proven ability to lead a research team to develop novel techniques for foundation model efficiency, with strong track record of industry impact.
• Deep expertise in at least one of: generative AI, LLMs, distributed ML systems, model optimization, or responsible AI, with emphasis on scaling and efficiency for large-scale neural networks.
• Strong programming skills and demonstrated ability to write high-quality, efficient code in Python and PyTorch for research implementation and experimentation.
• Demonstrated ability to translate research innovation into scalable product capabilities in partnership with product and engineering teams.
• Excellent communication, leadership, and stakeholder management skills.

Nice-to-Haves

• Prior work at the intersection of systems and ML, such as distributed training frameworks, compiler/kernel optimization for deep learning workloads, or memory-/compute-efficient model design.
• Strong industry and academic network in large-scale ML, with collaborations or service at top conferences in ML and systems.
• Strong record of research impact—first-author publications at top ML/systems conferences (e.g., ICLR, ICML, NeurIPS, MLSys), influential open-source contributions, or widely used deployed systems.