Software Engineer (LLM Prompting & Orchestration) at XBOW
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Job Description
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Your Role
We’re looking for a Software Engineer with deep expertise in LLM prompting, orchestration, and software engineering. In this role, you’ll design and implement systems that coordinate large language models with real-world tasks, advancing the intelligence at the heart of our platform. You’ll collaborate with our AI, Engineering, and Security teams to create novel prompting strategies, build orchestration layers, and deliver production-ready systems in TypeScript.
This role is for someone who thrives in a fast-moving, research-driven environment and enjoys turning ambiguous ideas into robust, scalable solutions.
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XBOW is a trailblazing cybersecurity company that has engineered the world’s first fully autonomous offensive security platform. Operating as a remote-first, globally distributed team, the company recently secured a massive $120M Series C funding round to scale its "autonomous hacker." Under the hood, XBOW utilizes advanced, agentic AI to execute targeted, machine-scale penetration tests that explore deep attack paths far faster than humanly possible. Unlike traditional vulnerability scanners that generate massive amounts of theoretical noise and false positives, XBOW independently validates every single potential finding through actual, real-world exploitation—providing security teams with reproducible proof of breach risk. Their technology has been rigorously validated on top industry bug bounty programs like HackerOne, proving its ability to uncover highly complex, original vulnerabilities in production-grade applications. Their primary target audience spans enterprise security teams, DevSecOps professionals, and CISOs who are struggling to keep pace with AI-native software development and the around-the-clock threat of AI-enabled attackers. What sets XBOW apart in the Infosec market is its ability to deliver the depth and results of a premium, manual pentesting engagement in a fraction of the time, allowing human experts to focus purely on high-level judgment and remediation rather than manual discovery.
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