Security Engineer, ML Infrastructure

Why work at Higgsfield AI?

Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands.

We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.

About the role:

Higgsfield AI is looking for a highly technical, hands‑on ML Security Engineer to join Higgsfield Security and own security across our GPU clusters and AI/ML infrastructure, from model training to inference and production deployment.

In this role, you will work as part of the Security team and closely with our ML and Infrastructure teams to design, implement, and operate security controls that protect models, data, and underlying infrastructure. Your core responsibilities will include securing GPU compute environments, assessing infrastructure and AI/ML vendors, and enforcing security requirements through technical controls, deployment policies, and verified remediation.

We're looking for someone who combines deep security engineering expertise with a practical, solutions-oriented mindset. The ideal candidate is comfortable working across cloud environments, Kubernetes, Linux, and GPU infrastructure, and understands how to balance security, performance, and reliability in a fast‑moving AI environment.

This role carries direct ownership of GPU infrastructure security and the security requirements that third‑party providers must meet to support Higgsfield’s workloads and data.

What you'll do:

AI/ML & GPU Infrastructure Security

• Own the security of GPU clusters supporting distributed training and production inference across cloud and rented datacenter environments.

• Harden GPU hosts, operating systems, drivers, container runtimes, Kubernetes clusters, and workload scheduling configurations; reduce privileged access and unnecessary exposure.

• Secure distributed training environments and GPU workloads through isolation, access controls, and data movement restrictions. Validate isolation between tenants and workloads across compute, networking, and storage, including shared GPU environments where applicable.

• Design and implement controls to protect sensitive datasets, model checkpoints, weights, and customer data.

• Strengthen inference security through tenant isolation, service authorization, secure model loading.

• Partner with ML and Platform Engineering teams to integrate security into infrastructure design and development workflows.