Research Engineer (Cybersecurity Reinforcement Learning)

• 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

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents

• Inclusive fertility benefits via Carrot Fertility

• 22 weeks of paid parental leave

• Flexible paid time off and absence policies

• Mental health support for you and your dependents

• Competitive salary and equity packages

• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant

• Retirement plans with competitive matching

• Life and income protection plans

• $500/month flexible wellness and time saver stipend

• Commuter benefits

• Annual education stipend

• Home office stipends

• Relocation support for those moving for Anthropic

• Daily meals and snacks in the office

• Have strong software engineering skills

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

• Can balance research exploration with engineering implementation

• Have experience in cybersecurity research

• Have experience with machine learning

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

• Familiarity with RL techniques and environments

• Experience participating in or building CTF competitions and cyber ranges

• Academic research experience in cybersecurity

• Familiarity with LLM training methodologies