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DatabricksDatabricksSan Francisco, CA

Principal Research Scientist – Scaling

Leads research team advancing LLM scaling, post-training, RL, and inference efficiency. Drives innovations in optimization, distributed systems, and production integration using Python/PyTorch, with deep expertise in large-scale ML.

280k – 350k
HybridAI Research

About the role

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 aligned with strategic objectives, prioritizing foundation model efficiency and large-scale training/inference.
  • Drive algorithmic innovations for large-scale neural network training/inference, including novel optimizers, low-precision techniques, and model adaptation methods.
  • Optimize end-to-end ML systems for distributed training/RL, memory/compute efficiency via collaboration with systems/platform teams.
  • Partner with product/engineering to translate research into customer-impacting capabilities.
  • Foster scientific excellence, reproducible experimentation, and knowledge sharing.
  • Represent research externally via publications, talks, and collaborations.
  • Mentor and develop research scientists/engineers.

What You Will Do

  • Define/lead research programs on foundation model efficiency (optimizer design, low-precision training/inference, scalable architectures, efficient adaptation).
  • Oversee large-scale experiments, benchmarking, and trade-off evaluation (quality, latency, throughput, cost).
  • Work hands-on with Python/PyTorch for research implementation, prototyping, and production integration.
  • Collaborate on distributed training, parallelism, memory management, hardware utilization.
  • Establish metrics/evaluation protocols for scaling research (training efficiency, inference cost, energy usage).
  • Champion responsible deployment ensuring model reliability/safety.

Requirements

  • Proven leadership of research teams developing novel foundation model efficiency techniques with industry impact.
  • Deep expertise in generative AI, LLMs, distributed ML systems, model optimization, or responsible AI, emphasizing scaling/efficiency.
  • Hands-on leadership with strong Python/PyTorch programming skills.
  • Ability to translate research into scalable product capabilities.
  • Excellent communication, leadership, stakeholder management skills.

Nice to Have

  • Experience at systems/ML intersection (distributed training frameworks, compiler/kernel optimization, memory/compute-efficient design).
  • Strong network in large-scale ML with conference service/collaborations.
  • Record of research impact (top ML/systems publications, open-source contributions, deployed systems).

Skills

PyTorchPythonLLMsDistributed TrainingModel OptimizationLow-Precision TrainingRlNeural NetworksFoundation ModelsScalable Architectures

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