Research Scientist/Engineer, Autonomous Security, DeepMind

DeepMind Technologies Limited; Mountain View (CA); On-site; USD 174,000 - 252,000; Full time

Job summary

DeepMind Technologies Limited is seeking a Research Scientist in Mountain View to design post-training methodologies for Gemini, including reinforcement learning from execution feedback, and to build scalable environments for security-focused research. You will architect autonomous agent systems and develop end-to-end evaluation benchmarks across offensive, defensive, and vulnerability tasks.

You will contribute to translating research into defenses and customer products, while publishing

Qualifications

• PhD in CS, cybersecurity, ML, or related field, or equivalent practical experience.
• 4 years of Python and ML framework experience (PyTorch, JAX, TensorFlow) training, fine-tuning, evaluating foundation models.
• 3 years applying ML or automated reasoning to cybersecurity, systems, program analysis, or code generation.
• 2 years in LLM post-training, including supervised fine-tuning, execution-feedback RL, and multi-step trajectory reward modeling.

Responsibilities

• Design and implement post-training methodologies for Gemini, including reinforcement learning from execution feedback and multi-step trajectory reward modeling.
• Build scalable simulated environments and synthetic data pipelines for complex security workflows and high-signal training trajectories.
• Architect autonomous agent harnesses with multi-step planning, tool orchestration, and decision-making under adversarial conditions.
• Develop end-to-end evaluation benchmarks measuring frontier capabilities and trajectory fidelity across offensive, defensive, and vulnerability tasks.
• Transition research into Google's internal defenses and customer products, and contribute to technical reports and publications.

Skills

Python

ML frameworks (PyTorch/JAX/TF)

LLM post-training

Reinforcement learning in security/AI

Education

PhD in Computer Science / related field

Tools

PyTorch

JAX

TensorFlow

Job description

Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA; San Francisco, CA, USA.

Minimum qualifications

• PhD in Computer Science, Cybersecurity, ML, a related field, or equivalent practical experience.
• 4 years of experience in Python and ML frameworks (PyTorch, JAX, or TensorFlow) training, fine-tuning, and evaluating foundation models.
• 3 years of experience applying ML or automated reasoning to cybersecurity, systems, program analysis, or code generation.
• 2 years of experience in LLM post-training, including supervised fine-tuning, execution-feedback reinforcement learning, and multi-step trajectory reward modeling.

Preferred qualifications

• Experience architecting autonomous agent systems, focusing on multi-step planning, tool orchestration, reward modeling, and reinforcement learning.
• Experience building scalable simulated execution environments, evaluation harnesses, or synthetic data pipelines for training and benchmarking foundation models.
• Deep domain expertise in one or more specialized cybersecurity areas, such as vulnerability discovery and automated patching, offensive security operations (red teaming), or advanced threat detection.
• Track record of published research at ML or cybersecurity venues, or a demonstrated history of deploying AI systems in production security environments.

About The Job

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work.

As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Responsibilities

Learn more about benefits at Google .

• Design and iterate on post-training methodologies for Gemini, including reinforcement learning from execution feedback, multi-step trajectory reward modeling, and supervised fine-tuning on curated security datasets.
• Build scalable simulated environments and synthetic data pipelines to execute complex security workflows and generate high-signal training trajectories at scale.
• Architect autonomous agent harnesses capable of multi-step planning, tool orchestration, and decision-making under adversarial conditions.
• Develop end-to-end evaluation benchmarks that measure frontier capability limits and trajectory fidelity across realistic offensive, defensive, and vulnerability tasks.
• Transition research innovations into Google's internal defenses and customer products, while contributing to technical reports and ML and security publications.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .