Machine Learning Engineer, Safety

fal is looking for a Machine Learning Engineer to own the ML and the ML infrastructure that power our safety systems end-to-end, from the models that detect harmful content and misuse to the pipelines and infrastructure that run them reliably at scale. This is a dedicated, hands-on engineering role sitting on the Trust & Safety team, working alongside our safety engineering function to keep detection capability ahead of a fast-growing platform with 1,000+ models.

Responsibilities

Design, build, and maintain the ML models and the ML infrastructure behind fal's safety and abuse-detection systems, end-to-end; Improve the accuracy, coverage, latency, and scalability of detection pipelines across the platform; Partner with Security and Infrastructure Engineering to integrate safety systems deeply into core platform infrastructure; Evaluate and integrate third-party safety tooling and vendor models where it makes sense; Stay current with the ML safety/detection landscape and bring new techniques and infrastructure patterns into fal's stack; Have access to the GPU cluster for inference and evaluation; Work alongside a team quickly iterating on and deploying new AI breakthroughs while ensuring speed never comes at the cost of safety

Requirements

Prior hands-on experience in trust & safety, content moderation, or abuse/detection systems; Strong end-to-end engineering fundamentals, comfortable owning both the ML and the infrastructure that serves it in production; Comfortable owning ambiguous, high-stakes problems with limited precedent; Based in San Francisco; fal works in-person, 5 days a week

Benefits

Interesting and challenging work; Competitive salary and equity; Comprehensive benefits package; A lot of learning and growth opportunities; Relocation assistance to San Francisco; Health, dental, and vision insurance (US); Regular team events and offsite