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Salary: USD 180,000 - 250,000 / annual
Fal is a generative media infrastructure platform powering the next generation of AI products. The company provides high-performance inference, orchestration, and observability tools that enable developers and enterprises to move from idea to production at scale.
You will own the ML and ML infrastructure powering fal's safety systems end-to-end. This is a dedicated, hands-on engineering role on the Trust & Safety team, working alongside safety engineering to keep detection capabilities ahead of a fast-growing platform hosting 1,000+ models.
Key responsibilities:
- Design, build, and maintain ML models and infrastructure for safety and abuse-detection systems across the entire platform
- Improve accuracy, coverage, latency, and scalability of detection pipelines
- Partner with Security and Infrastructure Engineering teams to integrate safety systems into core platform infrastructure
- Evaluate and integrate third-party safety tooling and vendor models where appropriate
- Stay current with ML safety and detection landscape, bringing new techniques and infrastructure patterns into fal's stack
- Leverage access to fal's massive GPU cluster for inference and evaluation
- Work with core technologies including Python, PyTorch, Diffusers, Kubernetes, and the fal Python SDK
- Ensure that the platform's speed of innovation never comes at the cost of safety
Requirements:
- Prior hands-on experience in trust & safety, content moderation, or abuse/detection systems (required)
- Strong end-to-end engineering fundamentals; comfortable owning both ML and the infrastructure that serves it in production
- Ability to own ambiguous, high-stakes problems with limited precedent
- Based in San Francisco; fal operates in-person, 5 days per week
