Research Engineer – Cybersecurity RL

San Francisco, CA | New York City, NYFull-TimeMid-levelSoftware Engineering

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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.

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.

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.

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.
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.

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.
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.

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.