Build the multi-agent reasoning systems that plan, hypothesize, and validate attack paths autonomously.
Cyrion’s core product is a swarm of coordinated LLM-driven agents that reason about attack surfaces the way a human red teamer would. You’ll own pieces of that reasoning loop — from hypothesis generation to confidence scoring to validation handoff.
What you'll do
- Design and train/prompt-engineer agent components for recon synthesis, hypothesis generation, and exploit chaining.
- Build evaluation harnesses that measure agent accuracy against known-ground-truth environments.
- Improve agent-to-agent coordination and shared memory across the swarm.
- Reduce false-positive rate by tightening the confidence-scoring and validation handoff logic.
- Ship incrementally — this is a production system with real customers, not a research sandbox.
What we're looking for
- 3+ years building production ML or LLM-backed systems.
- Strong Python; comfortable with modern agent/orchestration frameworks.
- Experience with retrieval, tool-calling, or multi-step planning architectures.
- A security or adversarial-ML background is a strong plus but not required — we will pair you with offensive researchers.
- Able to work within ±4 hours of CET for team overlap.
Nice to have
- Experience fine-tuning or evaluating open-weight models
- Published work on agent evaluation or red-teaming of LLMs
Competitive salary plus meaningful early equity, full health coverage, a $2,000/yr learning budget, and your choice of hardware.
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