# Research Scientist, Gemini Safety

**Company**: Google DeepMind
**Location**: Zurich, Switzerland
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q15733006

**Apply**: https://job-boards.greenhouse.io/deepmind/jobs/7731944
**Canonical**: https://yubhub.co/jobs/job_d2f5b1e5-545

## Description

We're seeking a versatile Research Scientist to join our Gemini Safety team. As a Research Scientist, you will apply and develop data and algorithmic cutting-edge solutions to advance our latest user-facing models. Your work will focus on advancing the safety and fairness behavior of state-of-the-art AI models, driving the development of foundational technology adopted by numerous product areas, including Gemini App, Cloud API, and Search.

Key responsibilities include:

- Post-training/instruction tuning state-of-the-art LLMs, focusing on text-to-text, image/video/audio-to-text modalities and agentic capabilities

- Exploring data, reasoning, and algorithmic solutions to ensure Gemini Models are safe, maximally helpful, and work for everyone

- Improve Gemini's adversarial robustness, with a focus on high-stakes abuse risks

- Design and maintain high-quality evaluation protocols to assess model behavior gaps and headroom related to safety and fairness

- Develop and execute experimental plans to address known gaps, or construct entirely new capabilities

- Drive innovation and enhance understanding of Supervised Fine Tuning and Reinforcement Learning fine-tuning at scale

To succeed as a Research Scientist in the Gemini Safety team, we look for the following skills and experience:

- PhD in Computer Science, a related field, or equivalent practical experience

- Significant LLM post-training experience

- Experience in Reward modeling and Reinforcement Learning for LLMs Instruction tuning

- Experience with Long-range Reinforcement learning

- Experience in areas such as Safety, Fairness, and Alignment

- Track record of publications at NeurIPS, ICLR, ICML

- Experience taking research from concept to product

- Experience with collaborating or leading an applied research project

- Strong experimental taste: Good judgment regarding baselines, ablations, and what is worth testing

- Experience with JAX

## Skills

### Required
- PhD in Computer Science
- LLM post-training experience
- Reward modeling and Reinforcement Learning for LLMs Instruction tuning
- Long-range Reinforcement learning
- Safety, Fairness, and Alignment
- NeurIPS, ICLR, ICML publications
- Research from concept to product
- Collaborating or leading an applied research project
- JAX