Member of Technical Staff, ML Security

About this role

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 MTS, ML Security 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.

Vendor Security Assessment & Enforcement

- Lead technical security assessments of GPU infrastructure providers, datacenter operators, and other AI security vendors that process Higgsfield data and support AI workloads.

- Evaluate vendor architecture and evidence covering tenant isolation, privileged access, data handling and retention, use of customer data for model training, encryption, vulnerability management, and incident response.

- Translate security requirements into enforceable technical controls, including approved configurations, and continuous checks for configuration drift.

- Track internal and vendor security findings to closure with accountable owners and deadlines, validate remediation, and escalate unmet requirements. Document any approved exceptions with compensating controls and expiration dates.

Cloud & Platform Security

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

- Secure infrastructure provisioning, configuration management, and access to third-party infrastructure providers. Restrict and audit vendor and administrator access to cluster management interfaces and production systems.

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

- Identify and remediate vulnerabilities and configuration risks across production systems, including GPU hosts, drivers, and container runtimes, coordinating changes with infrastructure owners to maintain service reliability.

Security Operations & Risk Management

- Develop and maintain detection and response capabilities for infrastructure and AI-specific security threats, including unauthorized GPU workloads, privilege escalation, and suspicious data transfers.

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

- Partner cross-functionally to implement practical security improvements that account for the performance, scalability, and reliability requirements of GPU workloads.

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.

- Practical experience securing or operating GPU clusters or distributed AI/ML infrastructure, with an understanding of training and inference workloads, GPU drivers and runtimes, and workload isolation.

- 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 conducting technical vendor security assessments, evaluating supporting evidence, and driving remediation with external providers.

- Ability to identify, investigate, and remediate security vulnerabilities and infrastructure threats.

- Proficiency in scripting or programming languages such as Python, Go, or Bash.

- Experience partnering with Infrastructure, Platform, or ML Engineering teams to implement security…