Senior Genai Research Scientist - AI Efficiency & Optimization

Databricks Databricks · Data AI · San Francisco, CA · Engineering - Pipeline

Research Scientist focused on foundation model efficiency in training and inference, optimizing ML systems for distributed training, memory, and compute efficiency. Requires MS/PhD in CS with ML/systems foundations, Python/PyTorch expertise, and a publication record in top ML/systems conferences.

What you'd actually do

  1. Define and lead independent research agendas on foundation model efficiency in model training and reinforcement learning, conducting experiments to empirically validate hypotheses and benchmark against state-of-the-art approaches
  2. Drive algorithmic innovations for large-scale neural network training or inference (e.g., novel optimizers, low-precision techniques, model adaptation methods)
  3. Optimize ML systems for distributed training, memory efficiency, and compute efficiency through hands-on implementation.

Skills

Required

  • MS/PhD in Computer Science or related field
  • Strong foundations in machine learning and systems
  • Python
  • PyTorch
  • Research implementation and experimentation

Nice to have

  • First-author publications at top ML/systems conferences (ICLR, ICML, NeurIPS, MLSys) focused on optimization or efficiency

What the JD emphasized

  • strong foundations in machine learning and systems
  • high-quality, efficient code in Python and PyTorch
  • first-author publications at top ML/systems conferences (ICLR, ICML, NeurIPS, MLSys) focused on optimization or efficiency

Other signals

  • foundation model efficiency
  • algorithmic innovations
  • distributed training
  • memory efficiency
  • compute efficiency