Research Engineer / Scientist, Frontier Red Team (Cyber)

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 Frontier Red Team (FRT) is a small, focused technical research team within Anthropic's Policy organization. Our goal is to make the entire world safer in an era of advanced AI by understanding what these systems can do and building the defenses that matter. In 2026, we're focused on researching and ensuring safety with self-improving, highly autonomous AI systems, especially ones related to cyberphysical capabilities. See our previous related work on exploits, partnering with Mozilla, and zero days. This is early-stage, high-conviction research with the potential for outsized impact — Glasswing is one example.

In the last year, we've seen compelling signs that LLMs and agents are increasingly capable of novel cyber capabilities. We think 2026 will be the year where models reach expert-level, even superhuman, in several cybersecurity domains. This is a novel and massive threat surface. As a Research Scientist on FRT focusing on cyber, you'll build the tools and frameworks needed to defend the world against advanced AI-enabled cyber threats. Senior candidates will have the opportunity to shape and grow Anthropic's cyberdefense research program, working with Security, Safeguards, Policy, and other partner teams. This work sits at the intersection of AI capabilities research, cybersecurity, and policy—what we learn directly shapes how Anthropic and the world prepare for AI-enabled cyber threats.

• Develop systems, tools, and frameworks for AI-empowered cybersecurity, such as autonomous vulnerability discovery and remediation, malware detection and management, network hardening, and pentesting
• Design and run experiments to elicit and evaluate autonomous AI cyber capabilities in realistic scenarios
• Design and build infrastructure for evaluating and enabling AI systems to operate in security environments
• Translate technical findings into compelling demonstrations and artifacts that inform policymakers and the public
• Collaborate with external experts in cybersecurity, national security, and AI safety to scope and validate research directions
• Senior candidates will also set research strategy, define what problems are worth solving, own the technical roadmap, and manage relationships with cross-functional partners

You may be a good fit if you have deep expertise in cybersecurity or security research, are driven to find solutions to complex, high-stakes problems, have experience doing technical research with LLM-based agents or autonomous systems, have strong software engineering skills particularly in Python, can own entire problems end-to-end, design and run experiments quickly, thrive in collaborative environments, care deeply about AI safety, are comfortable working on sensitive projects, and have proven ability to lead cross-functional security initiatives. Strong candidates may also have experience with offensive security research, vulnerability research, or exploit development; research or professional experience applying LLMs to security problems; a track record in competitive CTFs, bug bounties, or other security-related competitions; experience building security tools or automation; a track record of building demos or prototypes; experience working with external stakeholders; and familiarity with AI safety research and threat modeling.

Annual Salary: $320,000—$485,000 USD

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space. We support relocation.

Apply via the provided link. Note: We are exclusively hiring in SF. We support relocation, but all hires must relocate before starting. Visa sponsorship is available on a case-by-case basis.