Machine Learning Engineer, Safety

Responsibilities

• Design, build, and maintain ML models and production infrastructure for safety and abuse-detection systems.
• Improve the accuracy, coverage, latency, and scalability of detection pipelines across the platform.
• Partner with Security and Infrastructure Engineering to integrate safety systems into core platform infrastructure.
• Evaluate and integrate third-party safety tooling and vendor models.
• Track developments in the ML safety and detection landscape and introduce relevant techniques and infrastructure patterns.
• Use fal’s GPU cluster for inference and evaluation while deploying new safety capabilities at scale.

Requirements

• Hands-on experience in trust and safety, content moderation, or abuse/detection systems is required.
• Strong end-to-end engineering fundamentals, including ownership of ML systems and the infrastructure that serves them in production.
• Ability to own ambiguous, high-stakes problems with limited precedent.

Benefits

• Competitive salary and equity.
• Comprehensive benefits package including US health, dental, and vision insurance.
• Relocation assistance to San Francisco.
• Learning and growth opportunities, regular team events, and offsites.
• In-person work in San Francisco five days per week.