Senior Software Engineer, Agentic Systems

We are a fusion of former U.S. Special Operations cyber operators, startup engineers, and formerly frustrated cybersecurity practitioners. We're committed to helping solve our common security problems: ineffective security tools, false positives resulting in alert fatigue, blind spots, "checkbox" security culture, t...

We're building an autonomous, black-box web application penetration tester. It crawls and attacks real production websites the way a skilled human pentester would, finding broken access control, injection, XSS, SSRF, SSTI, and more, under a strict production-safe, no-false-positives mandate.

We have deep offensive expertise on this team: people who know exactly how to find and exploit these vulnerabilities by hand. What we need is an engineer who can turn that expertise into autonomous agent capability, the reasoning, orchestration, tooling, and evaluation that lets an LLM-driven agent do this work reli...

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5+ years building production software, with strong Python.
Hands-on experience building LLM-powered applications or agents, tool use / function calling, structured outputs, multi-step orchestration, and the glue that makes it all hold together.
A track record of making LLMs reliable in production, you've wrestled nondeterminism, designed around model limitations, and shipped something that worked when it mattered.
Real experience with evaluation: you've built or owned the harness that tells you whether a model or agent change is an improvement, not just a vibe.
Strong instincts for prompt and context engineering, and the judgment to keep the model's job small and well-scoped.
Solid software fundamentals — testing, observability, and the discipline to keep a complex agent debuggable.
Ownership mentality, comfortable owning a critical, fast-moving subsystem end to end.
 
Working knowledge of web application security, broken access control, IDOR/BOLA, SQLi, XSS, SSRF, SSTI, enough to collaborate fluently with offensive engineers.
Experience building eval harnesses or benchmarks specifically for agents (synthetic environments, CVE-based test targets, capture-the-flag-style scoring).
Experience with agent frameworks, and strong opinions about when not to reach for one.
Familiarity with graph data models (e.g., Neo4j) for representing application state and attack context.
 
You've shipped an autonomous agent that did real, valuable work unattended in production, and you have scar tissue from making it trustworthy.
You've designed evaluation systems that actually drove improvement, closed the loop between "we changed something" and "it measurably got better."
You pair an offensive-security mindset (CTF, bug bounty, pentesting, or research background) with the engineering chops to turn that intuition into a reliable system.
You have hands-on experience with agent fine-tuning or RL (SFT, GRPO, reward design for tool-using agents) and a grounded view of when it's worth it versus improving the harness.
You've published or spoken on agent reliability, evaluation, or autonomous security tooling.