# Sr. Staff AI Research TLM - AI Systems

**Company**: Databricks
**Location**: Mountain View, California; San Francisco, California
**Work arrangement**: onsite
**Experience**: staff
**Job type**: full-time
**Salary**: $270,000-$340,000 USD
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q18350420

**Apply**: https://job-boards.greenhouse.io/databricks/jobs/8557780002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_8312ba1d-2ed

## Description

As a Principal Research Scientist – Scaling, you will lead a team of world-class researchers and engineers to advance the state of the art in large-scale machine learning, focusing on post-training, RL and inference efficiency, optimization, and scaling.

You will define and execute a research roadmap that advances the Databricks AI platform and delivers tangible improvements to how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders to bring cutting-edge methods into production.

Responsibilities:

- Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with a particular 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 your team in rigorous empirical validation against state-of-the-art approaches.

- Optimize end-to-end ML systems for distributed training and RL, memory efficiency, and compute efficiency through close collaboration with core systems and platform teams, ensuring that research ideas translate into performant, reliable infrastructure.

- Partner with product and engineering to translate research breakthroughs, especially around scaling and efficiency, into customer-impacting capabilities in the Databricks AI platform.

- Foster a culture of scientific excellence and openness, including high-quality research practices, reproducible experimentation, and effective internal knowledge sharing across Databricks AI.

- Represent Databricks AI research externally through top-tier publications, conference talks, and collaborations with academia and the open-source community, with a focus on optimization and efficiency for large-scale models.

- Mentor and develop talent, providing both technical guidance (research agendas, experimentation, implementation) and career development support for research scientists and engineers.

Nice to Have:

- Prior work at the intersection of systems and ML, such as distributed training frameworks, compiler and kernel optimization for deep learning workloads, or memory-/compute-efficient model design.

- Strong industry and academic network in large-scale ML, with ongoing collaborations or service (e.g., PC/area chair) at top conferences in ML and systems.

- A strong record of research impact,such as first-author publications at top ML/systems conferences (e.g., ICLR, ICML, NeurIPS, MLSys), influential open-source contributions, or widely used deployed systems,especially in optimization or efficiency.

## Skills

### Required
- Generative AI
- Large Language Models (LLMs)
- Distributed Machine Learning Systems
- Model Optimization
- Responsible AI

### Nice to have
- Prior work at the intersection of systems and ML
- Strong industry and academic network in large-scale ML
- A strong record of research impact

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/databricks/jobs/8557780002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)