Principal AI Research Scientist, Research Director - AI Scaling (LLM)

Location: Mountain View, California or San Francisco, California, United States

Salary: $270,000–$340,000 USD annually

Company

Databricks builds a unified Data + AI Platform that helps organizations develop and scale data applications, analytics, AI models, and agents.

What you will do

• Lead and grow a multidisciplinary research team focused on LLM scaling, foundation model efficiency, and systems performance.
• Define and execute research programs covering optimizer design, low-precision training and inference, scalable architectures, and efficient model adaptation.
• Oversee large-scale experiments, benchmarking, evaluation protocols, and trade-off analysis across quality, latency, throughput, cost, and energy usage.
• Implement research prototypes and production integrations using Python and PyTorch.
• Collaborate with distributed systems and infrastructure teams on parallelism, memory management, hardware utilization, and distributed training.
• Translate research advances into reliable, safe, customer-impacting capabilities for the Databricks AI platform.

Requirements

• Ability to work in Mountain View or San Francisco, California
• Experience leading research teams and developing novel techniques for foundation model efficiency.
• Deep expertise in generative AI, LLMs, distributed ML systems, model optimization, or responsible AI, with a focus on scaling and efficiency.
• Strong programming skills and experience writing high-quality, efficient Python and PyTorch code.
• Ability to translate research innovation into scalable product capabilities with product and engineering teams.
• Strong communication, leadership, and stakeholder management skills.

Nice to have

• Experience with distributed training frameworks, compiler or kernel optimization, or memory- and compute-efficient model design.
• Industry or academic collaborations in large-scale machine learning and participation in leading ML or systems conferences.
• Research publications, influential open-source contributions, or widely deployed systems in optimization or efficiency.

Culture & Benefits

• Collaborative research environment spanning AI research, product, data, systems, and infrastructure teams.
• Support for reproducible experimentation, scientific excellence, and internal knowledge sharing.
• Opportunities to publish research, speak at conferences, and collaborate with academia and the open-source community.
• Comprehensive employee benefits and eligibility for annual performance bonus and equity.
• Inclusive workplace committed to equal employment opportunity.