# Sr. Staff AI Research TLM - AI Systems

**Company:** [Databricks](https://hotfix.jobs/companies/databricks)
**Location:** Mountain View, CA, San Francisco, CA
**Role:** AI Research
**Salary:** $270k – $340k/yr
**Skills:** Python, PyTorch, LLMs, Distributed Ml Systems, Model Optimization, Low-Precision Training/Inference, Rl (Reinforcement Learning), Foundation Model Efficiency, Distributed Training Frameworks, Compiler/Kernel Optimization
**Posted:** 2026-05-20

> Lead a world-class research team advancing LLM scaling, post-training, RL, and inference efficiency at Databricks AI. Drive research roadmap and translate breakthroughs into production systems while collaborating closely with engineering and product teams.

## Job Description

## 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.

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