Member of Technical Staff - Research (Cybersecurity/Benchmarking)

General Analysis is a security research lab focused on adversarial simulations, evaluations, and runtime protection. We are based in San Francisco and work across research, engineering, and product.

As a Research Engineer, you will be responsible for post-training models for adversarial capabilities using reinforcement learning. You will work with the entire training pipeline from data generation and environment design to evaluations.

You will tackle engineering challenges involving distributed systems, innovate new methods for data-constrained and low signal-to-noise environments, and make algorithmic improvements for applying trained models for adversarial simulations.

You may be a good fit if:

- You are comfortable with PyTorch, RL infrastructure, post-training infrastructure, or tools such as Tinker and verl.

- You have academic and/or industry experience with reinforcement learning or machine learning research, or are eager to learn.

- You have publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ACL, or similar.

- You have security experience, or are excited to build it here.

- You have a high level of agency, strong technical acumen, and clarity of thought.

You'll thrive here if:

- You are excited about applying novel research and new technology to real-world problems.

- You are excited to learn new domains and build deep context in AI security.

- You are results-oriented and can balance deep exploration with practical implementation.

- You proactively step outside defined boundaries to support the team's mission and unblock others.