Frontier Safety Mitigations Research Engineer, DeepMind

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.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

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.

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.