Staff Security Engineer

fal•San Francisco, CA

About The Position

fal is a generative media ecosystem providing infrastructure, tools, and model access for teams building AI products. The Staff Security Engineer will set technical direction for security across fal.ai, working across its GPU compute fleet, multi-cloud and Kubernetes environments, inference APIs, and AI systems. The role combines security architecture with hands-on implementation, making secure-by-default practices accessible to engineers. Proficiency in building with and using AI is central to the role. The engineer will secure the platform and AI systems, establish security architecture and priorities, lead design reviews, publish reusable secure patterns, manage vulnerabilities through remediation, and mentor engineers. The role also uses LLMs and agents to automate security work and develops guardrails for AI tools and agentic workflows.

Requirements

8+ years of engineering experience, including 5+ years in hands-on security engineering.
Deep knowledge of cloud security (AWS/GCP), Kubernetes, Linux, and identity and access systems.
Strong Python or Rust skills, including writing and shipping production code.
A record of security improvements adopted by engineering teams, including leading without formal authority.
Ability to explain security risk in business terms to engineers and executives.
Daily use of LLMs, agents, and AI coding tools to build, review, and automate security work.

Nice To Haves

Experience with GPU infrastructure, ML platforms, or multi-tenant compute.
Experience securing LLM-based systems or agent frameworks, or conducting AI red teaming.
Experience with sandboxing and isolation technologies such as gVisor, Firecracker, or Kata.
Experience supporting SOC 2 or ISO 27001 audits.

Responsibilities

Design isolation for multi-tenant GPU workloads, customer code, and model execution.
Protect model weights, customer data, and inference endpoints with identity, encryption, and access controls.
Harden cloud, Kubernetes, CI/CD, and the software supply chain.
Threat model inference, agentic workflows, and internal AI tools, including risks such as prompt injection, data leakage, and model supply chain issues.
Build guardrails for agents and AI tools, including scoped permissions, sandboxing, and audit logging.
Use LLMs and agents to automate security reviews, vulnerability triage, and remediation.
Own security architecture and the roadmap for the highest-risk areas.
Run design reviews and publish secure patterns that teams can adopt independently.
Manage vulnerabilities from discovery through prioritization and remediation.
Mentor engineers and raise the security bar across the company.