The role
From Google's own posting.
As a Research Engineering Manager, you will lead a team of Research and Software Engineers in our Responsible Frontier AI Research team. Your team will build the engineering foundations of DeepMind's Responsibility research portfolio. This includes automated platforms that discover, measure, and mitigate jailbreaks, and red-teaming tools used by subject-matter experts across DeepMind. It also includes defenses that help AI agents operate safely on the open web, and evaluations and tools that facilitate mitigations for severe harms, such as chemical, biological, radiological, and nuclear (CBRN) risks and harmful manipulation, as part of our Frontier Safety Framework.
Because the responsible AI landscape shifts quickly, you will need to be a highly adaptable leader who can step into any of these research areas, build context fast, and direct engineering effort where it matters most. You will balance research experiments with robust, scalable infrastructure that other teams rely on. You will also combine technical leadership with hiring, developing, and retaining an exceptional team.
You will be joining a specialized Applied Research and Responsibility team within Google while closely partnering with teams across DeepMind, Research, Product, and Policy to identify and address key issues in responsible AI. Our work remains deeply integrated with DeepMind.
Artificial intelligence will be one of humanity’s most transformative inventions. At 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.
Set and communicate priorities for multiple workstreams that support the broader responsibility organization's goals. Align strategy, processes, and decision-making across teams, and move engineering capacity between research projects as priorities change.
Set clear expectations with individuals, including tech leads, based on their level and role. Meet regularly to discuss performance and development, provide feedback and coaching, and hire and grow a high-performing team.
Develop the long-term technical goal and roadmap within, and often beyond, the scope of your teams, covering systems such as evaluation pipelines, red-teaming tools, autoraters, and mitigation training pipelines. Evolve it as frontier models become more capable, agentic, and multimodal.
Oversee system designs and review code to solve ambiguous problems. Set best practices for code quality, scalability, reliability, and secure handling of sensitive data.
Partner with Research Scientists, Subject-Matter Experts, and Product, Program, Policy, and Trust and Safety teams to turn research into evaluations and mitigations that inform model launch decisions. Present trade-offs and results to senior leadership.
Minimum qualifications:
Experience leading teams working on AI safety, security, or responsible AI research (e.g., adversarial robustness, red teaming, safety evaluations, or mitigation's for frontier models).
Experience with one or more of the following: automated red teaming or jailbreak discovery, LLM-based autoraters and evaluation design, safety classifiers or probes, prompt injection and agent security, or evaluation of severe-harm risks such as CBRN or Harmful Manipulation.
Preferred qualifications:
Master’s degree or PhD in Engineering, Computer Science, or a related technical field with a focus on adversarial machine learning or AI/ML security.
Track record of research or engineering achievements, such as publications in peer-reviewed venues, open-source projects, or public benchmarks.
Experience leading teams working on AI safety, security, or responsible AI research (e.g., adversarial robustness, red teaming, safety evaluations, or mitigation's for frontier models).
Experience with one or more of the following: automated red teaming or jailbreak discovery, LLM-based autoraters and evaluation design, safety classifiers or probes, prompt injection and agent security, or evaluation of severe-harm risks such as CBRN or Harmful Manipulation.