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 Security Engineer, AI Infrastructure to own and strengthen security across our AI/ML infrastructure, from data collection and model training to inference and production deployment.

In this role, you will work closely with our ML, Infrastructure, and Platform Engineering teams to design, implement, and operate security controls that protect our models, data, and underlying infrastructure.

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 is a highly impactful role with significant ownership over Higgsfield's security architecture, technical roadmap, and infrastructure security strategy.

What you'll do:

AI/ML INFRASTRUCTURE SECURITY

- Own security across the AI/ML lifecycle, including data ingestion, model training, inference, and deployment.

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

- Secure distributed training environments and GPU workloads through isolation, access controls, and data movement restrictions.

- Strengthen inference security through tenant isolation, service authorization, secure model loading, and protection of customer inputs and outputs.

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

CLOUD & PLATFORM SECURITY

- Design, implement, and maintain security controls across Linux, Kubernetes, cloud infrastructure, networks, and rented datacenter environments.

- Secure infrastructure provisioning, configuration management, and access to third-party infrastructure providers.

- Implement least-privilege access, workload identity, secrets management, encryption, and auditable access controls.

- Strengthen infrastructure security through secure CI/CD pipelines, dependency management, and infrastructure as code.

- Identify and remediate vulnerabilities and configuration risks across production systems.

SECURITY OPERATIONS & RISK MANAGEMENT

- Develop and maintain detection and response capabilities for infrastructure and AI-specific security threats.

- Investigate and respond to potential credential compromise, unauthorized data access, model theft, and compromised workloads.

- Conduct threat modeling and security assessments across AI/ML systems and infrastructure.

- Build and execute a prioritized security engineering roadmap focused on measurable risk reduction.

- Partner cross-functionally to implement practical security improvements without compromising system performance, scalability, or reliability.

Requirements:

- Strong hands-on experience in Security Engineering, Infrastructure Security, Platform Security, or a related technical discipline.

- Deep understanding of Linux, Kubernetes, networking, and cloud infrastructure security.

- Experience designing and implementing security controls across complex, distributed production environments.

- Strong knowledge of identity and access management, workload isolation, encryption, secrets management, and least-privilege principles.

- Experience securing CI/CD pipelines, infrastructure as code, and software depe