Software Engineer - AI Systems

Role overview Design and build the software that orchestrates large language models for an autonomous security platform. The work blends research-driven AI development with production engineering, focused on prompting strategies, evaluation infrastructure, and reliable end-to-end systems that surface real vulnerabilities with high precision.

Responsibilities - Design prompt flows and orchestration layers that coordinate LLMs with deterministic logic to deliver accurate, low-false-positive results. - Architect production-grade, testable, and maintainable AI-powered software in TypeScript. - Build experiments and evaluation frameworks that measure system performance at scale, and turn data into actionable conclusions. - Collaborate with AI researchers, security experts, and frontend and backend engineers to ship cohesive end-to-end features. - Own projects across the full lifecycle, from ideation and experimentation through deployment and production monitoring. - Track advances in LLM research and integrate promising techniques to improve speed and capability.

Requirements - Hands-on experience building software around LLMs, including prompting, agentic orchestration, fault tolerance, and integration with hard-coded logic. - Strong software engineering background with a track record of architecting reliable, maintainable production systems. - Proficiency in TypeScript, or demonstrated ability to ramp up quickly on a new language. - Structured, self-directed problem-solving skills and comfort working with incomplete information. - Ability to thrive in a fast-paced, agile environment with a research-lab mentality. - MSc or higher in computer science, mathematics, physics, or machine learning (or equivalent experience).

Nice to have - Experience in offensive security or applying LLMs to security problems. - Open-source contributions, especially around LLM frameworks or applications. - A PhD in machine learning, computer science, or a related field. - Prior startup experience and comfort navigating ambiguity.

Benefits and work setup - Competitive compensation plus a meaningful equity package. - Opportunity to help shape the function and grow alongside the team. - Remote-first setup, with regular team meetups and support for in-person collaboration. - Full-time contract. - Hiring process: introductory chat, engineering lead conversation, technical deep dive around a relevant case study, and a final meeting with the engineering lead.