# Principal Research Scientist - AI Scaling & Optimization

**Company:** [Databricks](https://hotfix.jobs/companies/databricks)
**Location:** Mountain View, CA, San Francisco, CA
**Role:** AI Research
**Salary:** $270k – $350k/yr
**Skills:** PyTorch, Python, LLMs, Distributed Training, Model Optimization, Low-Precision Training, Rl, Neural Networks, Scalable Architectures, Inference Optimization
**Posted:** 2026-05-01

> Leads research team advancing LLM scaling, efficiency, post-training, RL, and inference optimization. Drives innovations from research to production, requiring deep ML expertise, Python/PyTorch proficiency, and leadership in large-scale experiments.

## Job Description

## Responsibilities

- Lead and grow a multidisciplinary research team focused on LLM scaling, efficiency, and systems performance.
- Define scaling research roadmap aligned with strategic objectives, prioritizing foundation model efficiency and large-scale training/inference.
- Drive algorithmic innovations for neural network training/inference (optimizers, low-precision techniques, model adaptation) with empirical validation.
- Optimize end-to-end ML systems for distributed training, RL, memory/compute efficiency in collaboration with systems/platform teams.
- Partner with product/engineering to productionize research breakthroughs in scaling/efficiency.
- Represent research externally via publications, talks, collaborations.
- Mentor talent with technical/career guidance.

## What You Will Do

- Define/lead research programs on foundation model efficiency (optimizer design, low-precision training/inference, scalable architectures, adaptation methods).
- Oversee large-scale experiments, benchmarking, trade-off evaluation (quality, latency, throughput, cost).
- Write high-quality Python/PyTorch code for implementation, prototyping, 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 developing novel foundation model efficiency techniques with industry impact.
- Deep expertise in generative AI, LLMs, distributed ML systems, model optimization, or responsible AI (focus on scaling/efficiency).
- Strong programming in **Python** and **PyTorch** for research/implementation.
- Ability to translate research to scalable product capabilities.
- Excellent communication, leadership, stakeholder management.

## 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/publications (**ICLR**, **ICML**, **NeurIPS**, **MLSys**).
- Record of research impact via publications, open-source, deployed systems in optimization/efficiency.

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