Security Engineer, ML Infrastructure

Higgsfield•San Francisco, CA

3d•$200,000 - $300,000•Hybrid

About The Position

Higgsfield AI is seeking a hands-on Security Engineer to own and strengthen security across its AI/ML infrastructure, from data collection and model training through inference and production deployment. The role works closely with ML, Infrastructure, and Platform Engineering teams to design, implement, and operate controls that protect models, data, and underlying infrastructure. The position includes significant ownership of security architecture, the technical roadmap, and infrastructure security strategy. The successful candidate will help balance security, performance, and reliability across cloud environments, Kubernetes, Linux, GPU infrastructure, and rented datacenter environments. This is a hybrid role based in the San Francisco Bay Area, with three full days per week in the San Francisco office and remaining days worked remotely. Employees are expected to be available during agreed working hours and maintain sufficient overlap with the relevant team’s time zone.

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 dependencies.
Ability to identify, investigate, and remediate security vulnerabilities and infrastructure threats.
Proficiency in a scripting or programming language such as Python, Go, or Bash.
Experience partnering with Infrastructure, Platform, or ML Engineering teams to implement security solutions.
Strong technical ownership, sound judgment, and the ability to prioritize security risks in a fast-paced environment.
A hands-on, collaborative approach and ability to independently drive security initiatives from design through deployment.

Responsibilities

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, model 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.
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.
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.
Drive security initiatives independently from design through deployment.

Benefits

Competitive and thoughtfully structured compensation package aligned with impact, experience, and long-term growth.
Bonus eligibility and potential participation in the Company's discretionary annual bonus program.
Eligibility to participate in the company’s stock option program.
Medical insurance.
Dental insurance.
Vision insurance.
Competitive 401(k) retirement plan with company matching.
Additional benefits and perks.
Opportunities for professional growth as the company scales.
Opportunity to work on ambitious AI products alongside an experienced, fast-moving, international team.