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.
🏢 About 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.
🎯 The 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.
✅ Key Responsibilities
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.
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.
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.
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.
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.
📌 Required Qualifications
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.
⭐ Desirable Experience
Experience with security in generative AI or large-scale ML systems.
Knowledge of ML model security, adversarial attacks, and model theft prevention.
Experience with security automation, infrastructure as code, and CI/CD security.
Understanding of regulatory compliance requirements (e.g., GDPR, HIPAA, CCPA) as they apply to AI/ML systems.
Please let Higgsfield know that you found this role at devopsprojectshq.com as a way to support us, so we can keep providing you with awesome DevOps jobs.
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