Senior GenAI Research Scientist - AI Efficiency & Optimization

DatabricksData Lakehouse company

Mountain View, United States$166,000 - $230,000Senior

Data & AI

About the role

Develop and implement advanced AI techniques for model efficiency and optimization.

•Research Scientist on the Scaling team, you will be responsible for keeping up with the latest developments in deep learning and advancing the scientific frontier by creating new techniques that go beyond the state of the art.
•Key Responsibilities 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.
•Drive algorithmic innovations for large-scale neural network training or inference (e.g., novel optimizers, low-precision techniques, model adaptation methods).
•Optimize ML systems for distributed training, memory efficiency, and compute efficiency through hands-on implementation.
•Requirements MS/PhD in Computer Science or related field with strong foundations in machine learning and systems.
•Proven ability to write high-quality, efficient code in Python and PyTorch for research implementation and experimentation.
•Strong preference for candidates with first-author publications at top ML/systems conferences (ICLR, ICML, NeurIPS, MLSys) focused on optimization or efficiency.

PythonPyTorch

About the role

Develop and implement advanced AI techniques for model efficiency and optimization.

•Research Scientist on the Scaling team, you will be responsible for keeping up with the latest developments in deep learning and advancing the scientific frontier by creating new techniques that go beyond the state of the art.
•Key Responsibilities 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.
•Drive algorithmic innovations for large-scale neural network training or inference (e.g., novel optimizers, low-precision techniques, model adaptation methods).
•Optimize ML systems for distributed training, memory efficiency, and compute efficiency through hands-on implementation.
•Requirements MS/PhD in Computer Science or related field with strong foundations in machine learning and systems.
•Proven ability to write high-quality, efficient code in Python and PyTorch for research implementation and experimentation.
•Strong preference for candidates with first-author publications at top ML/systems conferences (ICLR, ICML, NeurIPS, MLSys) focused on optimization or efficiency.