Research Engineer, Cybersecurity Reinforcement Learning

About Horizons

The Horizons team leads Anthropic's reinforcement learning (RL) research and development, playing a critical role in advancing our AI systems. We've contributed to every Claude release, with significant impact on the autonomy, coding, and reasoning capabilities of Anthropic's models.

About the role

We're hiring for the Cybersecurity RL team within Horizons. As a Research Engineer, you'll help to safely advance the capabilities of our models in secure coding, vulnerability remediation, and other areas of defensive cybersecurity.

This role blends research and engineering, requiring you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers, engineers, and cybersecurity specialists across and outside Anthropic.

The role requires domain expertise in cybersecurity paired with interest or experience in training safe AI models. For example, you might be a white hat hacker who's curious about how LLMs could augment or transform your work, a security engineer interested in how AI could help harden systems at scale, or a detection and response professional wondering how models could enhance defensive workflows.

You may be a good fit if you:

• Have experience in cybersecurity research.

• Have experience with machine learning.

• Have strong software engineering skills.

• Can balance research exploration with engineering implementation.

• Are passionate about AI's potential and committed to developing safe and beneficial systems.

Strong candidates may also have:

• Professional experience in security engineering, fuzzing, detection and response, or other applied defensive work.

• Experience participating in or building CTF competitions and cyber ranges.

• Academic research experience in cybersecurity.

• Familiarity with RL techniques and environments.

• Familiarity with LLM training methodologies.