- 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
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.
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.
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.
