fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.
As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.
About this role:
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.
What you’ll do:
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
You will have access to our massive GPU cluster for inference and evaluation
Some core technologies we use include Python, torch, diffusers, Kubernetes, and the fal Python SDK
You'll work alongside a team dedicated to quickly iterating on and deploying new AI breakthroughs — your job is to make sure that speed never comes at the cost of safety
Qualifications/Nice to have:
Prior hands-on experience in trust & safety, content moderation, or abuse/detection systems — required
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
What we offer at fal:
Interesting and challenging work
Competitive salary and equity
A lot of learning and growth opportunities
We offer relocation assistance to San Francisco.
Health, dental, and vision insurance (US)
Regular team events and offsite
Comp:
U.S. EQUAL EMPLOYMENT OPPORTUNITY INFORMATION:
fal provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law.
