Research Engineer, Frontier Safety Mitigations, DeepMind

In this role, you will de-risk model launches by defending against misuse domains (e.g., Cybersecurity, Chemical, Biological, Radiological, Nuclear, and Conventional Explosive [CBRNE], and Harmful Manipulation). You will build evaluations, conduct red-teaming, research and deploy mitigations (both in-model and out-of-model), and monitor emerging risks to enable the beneficial use of technology.

DeepMind is a dedicated scientific community, committed to ‘solving intelligence’ and ensuring technology is used for widespread public benefit. The Frontier Safety Mitigation team operates in a collaborative environment with a culture of support, dedication, and teamwork. The team takes the possibility of dangerous model capabilities seriously as AI advances. Proactively researching and implementing defense-in-depth mitigations is a critical part of the overall strategy for building safe AI.

You will join the Frontier Safety Mitigation team within the Gemini Safety team to build safety mitigations for frontier models. You will focus on building defenses against risks, contributing to DeepMind's Frontier Safety Framework commitments.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.

Minimum qualifications:

• 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.

Preferred qualifications:

• PhD in Computer Science, Machine Learning, or equivalent practical experience, or publications at venues (e.g., NeurIPS, ICLR, ICML, or EMNLP).
• 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 in adversarial machine learning, automated red-teaming, or model interpretability and probes.
• Experience collaborating on or leading applied ML projects, including LLM training, inference, and fine-tuning.
• Experience using AI coding agents with strong architectural judgment and with TPUs and JAX.
• Knowledge of AI control, chain-of-thought monitoring, monitorability, and related frontier safety research.