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# Research Engineer, Frontier Safety Mitigations, DeepMind

- **Company:** Google LLC
- **Location:** San Francisco, CA, United States
- **Salary:** $174,000.0 - $252,000.0
- **Contract:** Permanent contract
- **Skills:** Bioinformatics, Monitoring of Systems, Software Architecture, Software Engineering, Large Language Models, Information Technology, Data Analytics, DeepMind, Data Pipelines, Programming Languages
- **Published:** October 14, 2026
- **Apply:** https://www.sanfranciscogigs.com/job.asp?id=3445792124&tx=UT5854THV&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's degree or equivalent practical experience.
 * 5 years of experience with software development in one or more programming languages.
 * 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture., * Master's degree or PhD in Computer Science or a related technical field.
 * Experience in Large Language Model (LLM) development, fine-tuning, or safety evaluation methodologies.
 * Experience taking research from concept to product.
 * Experience in areas such as Safety and Alignment.

## Description

* Build advanced classifiers and data pipelines to detect misuse, owning the end-to-end process from automated evaluation to rapid model iteration.
 * Build cross-context monitoring systems to detect coordinated harms, developing novel signal aggregation methods across disparate user sessions to identify large-scale attack vectors.
 * Implement data-driven, semi-automated account-level response systems to detect, track, and apply strikes against persistent malicious actors using rich signals from production traffic.
 * Evaluate and secure agentic AI systems by developing threat models, creating testing environments, and deploying robust mitigations against frontier-level agentic hacking and long-horizon attacks.
 * Advance research in automated red-teaming and adversarial robustness, leveraging multi-turn/agentic attacks to systematically test for and uncover misuse vulnerabilities.

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