Staff Security Engineer, Infrastructure

🏢 Fal Ai · 📍 San Francisco · ⏰ FullTime

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Position Summary & Responsibilities

fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.

As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.

About the Role

We’re looking for a Security Engineer, Infrastructure to secure the core systems that power fal.ai’s platform: GPU compute, multi-cloud environments, networking, and data pipelines. You’ll operate across the full stack, from cloud and Kubernetes to identity, networking, and secrets, designing and implementing security controls that scale with a high-performance AI platform. This role is highly hands-on and systems-oriented, sitting at the intersection of security, infrastructure, and distributed systems.

What You’ll Do

Build & Harden Infrastructure Security

Design and implement security controls across:

Cloud infrastructure
Kubernetes and containerized workloads
Networking, service meshes, and edge systems
CI/CD pipelines and deployment systems
Secure compute environments for GPU workloads and model execution

Identity, Secrets & Access

Machine identity and workload authentication
Secrets management and encryption (e.g., Vault, KMS)
Least-privilege access and short-lived credentials
Implement Zero Trust principles across infrastructure

🎯 Candidate Preparation Guide For Fal Ai Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Evaluators obsess over extreme systems performance, ultra-low latency inference, and distributed GPU infrastructure optimization.

📋 Preparation Priorities (Key Areas to Clarify):

1. Treating GPU infrastructure as a black box managed entirely by cloud provider defaults.
2. Showing weakness in systems-level programming, memory management, or networking.
3. Focusing exclusively on model training while ignoring the realities of high-throughput inference.

✨ Recommended Role Alignment Hook:

I engineer high-throughput, ultra-low latency GPU inference pipelines that push generative models to their performance limits.

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