Want to know if this job is worth applying to?
San Francisco, California, United States
Onsite
Flow Engineering is an AI-native requirements platform for modern engineering organizations, enabling hardware teams to collaborate with AI agents to design, validate, and evolve complex systems with speed and rigor.
Our customers in defense, aerospace and more trust us with sensitive systems requirements. That means security is a first-order part of the product, so we’re looking for a Staff Software Engineer to own it.
This is our first dedicated security hire, so you'll define the security architecture, harden the platform, and build out the compliance posture. Since LLMs and agentic workflows sit at the center of what we do, you'll also lead how we secure them.
Own security architecture across the platform: threat modeling, secure design reviews, and the standards we build against.
Harden the full stack, from application security to cloud infrastructure (identity, network, secrets), tenant isolation, and data protection.
Drive SOC 2 and get us ready for the certifications defense and aerospace customers ask about, including export control and ITAR.
Secure our AI and agentic systems: prompt injection, data leaking through models, what actions agents are allowed to take, and guardrails.
Build the security tooling we need across detection, monitoring, vulnerability management, and CI/CD.
Stand up incident response and run it, from detection through postmortems.
Work with sales and product to get through security reviews and win customer confidence.
8+ years building production software, including running secure services at scale in the cloud.
Deep experience in application and cloud security (AWS, GCP, or Azure): identity and access, secrets, network security, data protection.
You've owned security in a regulated or high-trust environment and have implemented SOC 2 yourself. Export control, ITAR, or FedRAMP experience is a big plus.
Solid on threat modeling, secure architecture, and incident response.
You've secured AI or LLM systems.
You're comfortable being the security lead at a startup and setting the agenda yourself.
TypeScript/Node.js and Python for AI and backend services.
Modern LLM APIs and orchestration libraries for building agentic workflows.
Postgres and other managed cloud services for data and state.
Speed over everything: prototype AI workflows quickly, then harden what works.
Own, downscope, ship, iterate: one clear owner per feature, from prototype to production.
Fundamentals done well: evaluation, observability, and safety are part of the first version, not an afterthought.
Competitive salary and meaningful equity.
Health, dental, and vision coverage.
Flexible time off and support for experimentation, learning, and staying current with the AI ecosystem.
