Anthropic· AI Research & Engineering· San Francisco, CA | New York City, NY
Research Engineer, Cybersecurity Reinforcement Learning
Classified Tasks (9)
Automate 0%Augment 67%Human-Only 33%
Augment (6)
AI assists, human decides
Advance model capabilities in secure coding, vulnerability remediation, and other defensive cybersecurity domains.
technical
Design and implement reinforcement learning (RL) environments.
technical
Design, conduct, and analyze experiments and evaluations of models and RL environments.
analytical
Integrate code, datasets, and configurations into training pipelines and deliver research artifacts into production training runs.
operational
Run, monitor, and iterate on production training jobs and training workflows.
operational
Prototype algorithms and systems and translate prototypes into deployable, production-ready implementations.
technical
Human-Only (3)
Requires human judgment
Develop novel research approaches and implement them in production-quality code.
technical
Collaborate with researchers, engineers, and cybersecurity specialists within and outside Anthropic to coordinate research and engineering efforts.
communication
Apply cybersecurity domain knowledge to inform environment design, evaluation criteria, and research priorities.
analytical
Job description
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. 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. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $300,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may req