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Founding Engineer – Autonomous Cybersecurity Agents (AI/RL)

Stealth Startup • United States
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Building autonomous cybersecurity agents that attack and defend using reinforcement learning (RL). Seeking a founding engineer with deep offensive/defensive security expertise and AI/ML experience to own the technical core, including agent architecture, RL environments, and training pipelines. Founding cofounder equity and in-person role in San Francisco.

Key Highlights
Founding cofounder equity with low cash compensation
Own technical core including autonomous cyber agents, RL environments, and training pipelines
Focus on offensive/defensive security and AI/ML with real-world impact
Key Responsibilities
Develop autonomous cybersecurity agents using reinforcement learning
Design and build RL environments with real vulnerabilities
Create deterministic grading/oracle systems for agent verification
Train and optimize AI models for cybersecurity applications
Lead technical architecture and first hires
Technical Skills Required
Reinforcement Learning (RL) Cybersecurity (Offensive/Defensive) AI/ML Model Training
Benefits & Perks
Founding cofounder equity
Visa sponsorship for relocation to San Francisco
Direct ownership of technical thesis and architecture
Nice to Have
Strong bug-bounty, CVE, or CTF track record
Experience with agentic AI systems
Background in AI security (e.g., prompt injection, model/weight security)

Job Description

A repeat YC founder is building autonomous cybersecurity agents that attack and defend for real, trained in RL environments against real vulnerabilities.

Looking for a founding engineer with a deep offensive and defensive security background and real AI chops, who wants to own the technical core from day zero, with cofounder equity to match.

The bet.

Security is going autonomous. The agents that find, exploit, and patch vulnerabilities will be trained, not prompted. We build the RL environments where cyber agents learn against real vulnerabilities with deterministic grading, and we train the agents themselves. The verified environment is the moat, not the weights.

What you'll own

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