Compensation
Salary undisclosedDescription
About RunSybil
RunSybil is an AI security startup scaling offensive security services by automating hacker intuition. We’re building Sybil: an AI-powered penetration testing product that finds vulnerabilities faster than human hackers. Backed by strong investor support and early customer traction, our team is composed of experts from the likes of OpenAI, Meta, Mandiant, Palantir, Cruise, and Trail of Bits.
About this Role
We are seeking talented engineers intent on changing the security industry. If you have experience on fast-moving teams and pushing out high quality experiments: we want to talk to you.
What You’ll Do:
- Propose and implement technical initiatives based on product roadmap needs
- Lead technical design discussions, contribute to team decisions, and provide feedback on engineering approaches
- Translate ambiguous technical challenges into actionable plans, and execute them in collaboration with the team
- Serve as the cornerstone of our engineering culture by setting and upholding exemplary standards for code quality, practices, and craftsmanship
We’re Looking For Someone Who Has A Mix of The Following:
- Experience working with security data problems
- MS or PhD in a quantitative discipline is nice but certainly not required - strong bias for building product-informing experiments versus published works
- 4+ years of experience designing experiments and managing data infrastructure
- Understanding of both modern and classic machine learning techniques
- Equally comfortable with Jupyter notebooks and building data pipelines
- Seeks autonomy, creative problem-solving, and moving quickly in an ambiguous environment
Location:
Hybrid role based in New York City. Some travel may be required.
What We Offer:
- Competitive Salary: Attractive startup compensation package with equity options
- Benefits: Excellent medical, dental, and vision plans. Unlimited PTO
- Growth Opportunities and Early Impact: Work directly with and learn from industry leaders while driving significant impact in our fast-growing security startup
We understand not every applicant will meet every qualification. We value hard work and potential, and still want to hear from you!
Stack
Machine LearningData Engineering
- Posted
- Jul 7, 2025
- Last seen
- Jul 4, 2026
- First seen
- Jul 4, 2026
