Cloud & ML Infrastructure Security Engineer - Hybrid SF

Higgsfield; San Francisco (CA); Hybrid; USD 165,000 - 250,000; Full time

Benefits offered by this job

Stock options

Hybrid work model

Office in San Francisco

Job summary

Higgsfield AI is seeking a hands-on Infrastructure Security Engineer to own security across our cloud and ML infrastructure, sitting between Security, DevOps, and ML Engineering. You'll lead cloud/network security, vulnerability management, and access governance, shaping security guardrails that engineers ship with.

With 7+ years in security at high-growth startups, you will secure AWS, Kubernetes, and Terraform-based environments, and drive protection for ML workloads across training and

Qualifications

• 7+ years in infrastructure security or security engineering, including time at a high-growth startup.

Responsibilities

• Evaluate, implement, and maintain cloud security tooling.
• Own infrastructure and container vulnerability management across cloud environments, and drive remediation.
• Design and implement network segmentation to tighten internal security boundaries.
• Build centralized security monitoring across cloud infrastructure.
• Own access governance and credential management across cloud and internal tooling.
• Partner with Engineering, DevOps, and IT to raise identity and access management standards.
• Set the technical direction for infrastructure security and lead cross-functional projects turning standards into tooling and guardrails.

Skills

AWS

Kubernetes

Terraform

Security engineering

Networking

Cryptography

IAM/Access control

Tools

Terraform

Kubernetes

Job description

Higgsfield AI is seeking a hands-on Infrastructure Security Engineer to own security across our cloud and ML infrastructure, sitting between Security, DevOps, and ML Engineering. You'll lead cloud/network security, vulnerability management, and access governance, shaping security guardrails that engineers ship with.

With 7+ years in security at high-growth startups, you will secure AWS, Kubernetes, and Terraform-based environments, and drive protection for ML workloads across training and