Machine Learning Engineer, Safety

Summary by AIEngineer.careers

Machine Learning Engineer, Safety is a AI Safety Engineer role (full-time). with fal. in SAN FRANCISCO, US. Compensation shown: $180K–$250K. Imported listing (source: kindredventures.getro.com). Apply on the employer's site (kindredventures.getro.com).

Imported listing (source: kindredventures.getro.com) · Apply on kindredventures.getro.com

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SAN FRANCISCO · US

$180K–$250K

Full-time

MID

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The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.

Imported job description

Sourced from kindredventures.getro.com

fal is seeking a Machine Learning Engineer to lead the development and infrastructure of its Trust & Safety systems. This role focuses on building end-to-end ML models and pipelines for content moderation, abuse detection, and safety enforcement at scale. The engineer will work closely with security and infrastructure teams to integrate these safety mechanisms into fal's generative media platform.

Responsibilities

Design, build, and maintain ML models and infrastructure for safety and abuse-detection systems
Improve accuracy, coverage, latency, and scalability of detection pipelines
Partner with Security and Infrastructure Engineering to integrate safety systems into core platform infrastructure
Evaluate and integrate third-party safety tooling and vendor models
Stay current with ML safety/detection landscape and introduce new techniques

Requirements

Prior hands-on experience in trust & safety, content moderation, or abuse/detection systems
Strong end-to-end engineering fundamentals comfortable owning both ML and production infrastructure
Ability to handle ambiguous, high-stakes problems with limited precedent
Based in San Francisco; willing to work on-site 5 days a week

fal

San Francisco · US · 198+ employees

fal is a generative media platform that provides developers with a unified API to access, run, and fine-tune generative image, video, audio, and 3D models. It offers an inference engine designed for high-speed generative media and provides both serverless and dedicated compute solutions.