Build the future of offensive security with XBOW. Attackers are already using AI to move faster than defenders can react—we’re creating the platform that puts security ahead in the arms race. Our AI-powered system autonomously discovers, validates, and even exploits vulnerabilities, giving organizations proof-backed results in hours instead of weeks.
Requirements
- Strong experience with building software around LLMs: prompting, agentic orchestration, fault-tolerance, and integration of LLM parts with hard-coded logic.
- Strong software engineering skills: architecting and building production-grade software that runs reliably and can be maintained.
- Experience with TypeScript or proven ability to learn a new programming language quickly.
- Strong skills in structured and independently-driven problem-solving.
- Prior experience with the security sector, especially involving offensive security and/or applying LLMs in a security context.
- Contributions to open-source software, particularly within applications of or frameworks for LLMs.
- Strong theoretical background, e.g. a Ph.D in machine learning, computer science or math.
Responsibilities
- Build LLM-powered software that actually works, by designing prompt flows and orchestrations that ensures great performance with no false positives.
- Architect and build an AI-powered software stack that is production-grade, testable and maintainable.
- Design and build experiments and evaluation frameworks for performance testing of the system at scale.
- Collaborate with the rest of the AI team, with security experts, and both frontend and backend developers to create end-to-end systems that work and customers love.
- Own projects end-to-end: from basic ideation and experimentation to deployment and production monitoring.
- Continuously conduct research on how to harness the advancements in LLMs to make our system better and faster.
Other
- MSc or equivalent or higher in computer science, math, physics or machine learning.
- Comfortable with an energetic environment that mixes the fast-paced agile prioritisation of a startup with the curiosity mentality of a research lab.
- Eager to own projects and jump into the deep end, learning as you go.
- Curious, adaptable and collaborative.
- Startup experience and comfort with ambiguity.
