Autonomous Security ML Scientist & Engineer

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

Benefits offered by this job

Equity

Benefits

Job summary

Google DeepMind in the United States seeks a Research Scientist to design and deploy large-scale AI systems, including reinforcement learning from execution feedback, trajectory reward modeling, and supervised fine-tuning on security datasets. You will run experiments, prototype architectures, and publish findings in collaboration with universities and internal teams.

The role emphasizes building scalable simulation environments, autonomous-agent harnesses, and transitioning research into

Qualifications

• PhD in Computer Science, cybersecurity, ML, or related field, or equivalent practical experience.
• 4+ years of Python and ML frameworks experience for training, fine-tuning and 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 and RL with feedback.

Responsibilities

• Design and iterate on post-training methodologies for Gemini, including RL from execution feedback and supervised fine-tuning on security datasets.
• Build scalable simulated environments and data pipelines to train and evaluate complex security workloads.
• Architect autonomous agent systems with multi-step planning, tool orchestration, and adversarial decision-making.
• Develop end-to-end evaluation benchmarks for frontier capability and trajectory fidelity across offensive/defensive tasks.
• Translate research into Google's internal defenses and customer products, and contribute to reports and publications.

Skills

Python programming

ML frameworks

Reinforcement learning

Security-focused ML

Education

PhD in Computer Science, Cybersecurity, ML, or related field

Tools

PyTorch

JAX

TensorFlow

Job description

Google DeepMind in the United States seeks a Research Scientist to design and deploy large-scale AI systems, including reinforcement learning from execution feedback, trajectory reward modeling, and supervised fine-tuning on security datasets. You will run experiments, prototype architectures, and publish findings in collaboration with universities and internal teams.

The role emphasizes building scalable simulation environments, autonomous-agent harnesses, and transitioning research into