Frontier Safety Mitigations Research Engineer, DeepMind

About the job

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities

• Build advanced classifiers and data pipelines to detect misuse, and own 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.

Minimum qualifications:

• Bachelor's degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
• 5 years of experience in software development, including experience with Python.
• Experience with the research-to-deployment pipeline in a frontier AI environment.
• Experience working in a software engineering or research team.

Preferred qualifications:

• Experience with cybersecurity detection and response, building classifiers and anomaly detection systems at scale, taking safety defenses or mitigations from research concepts to scalable production systems.
• Experience collaborating on or leading applied ML projects, including LLM training, inference, and fine-tuning.
• Experience using AI coding agents with strong architectural judgement, and with TPUs and JAX.
• Background in adversarial machine learning, automated red-teaming, or model interpretability and probes.
• Knowledge of AI control, chain-of-thought monitoring, faithfulness, monitorability, and related frontier safety research.

• Bachelor's degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
• 5 years of experience in software development, including experience with Python.
• Experience with the research-to-deployment pipeline in a frontier AI environment.
• Experience working in a software engineering or research team.