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Fal

NLP-based sentiment & anomaly detection

Machine Learning Engineer - Safety

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Prior hands-on experience in trust and safety, content moderation, or abuse/detection systems is required.
  • Strong end-to-end engineering fundamentals, including the ability to own both machine learning and the infrastructure serving it in production.
  • Ability to own ambiguous, high-stakes problems with limited precedent.
  • Must be based in San Francisco and work in person five days a week.

Responsibilities

  • Design, build, and maintain the machine learning models and 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 appropriate.
  • Stay current with the machine learning safety and detection landscape and bring new techniques and infrastructure patterns into fal's stack.
  • Use the GPU cluster for inference and evaluation.

About the Company

Fal

NLP-based sentiment & anomaly detection

Fal.ai enables data-driven businesses to improve analytics using natural language processing and machine learning, focusing on sentiment analysis and anomaly detection within dbt data models. It analyzes text such as customer reviews, support tickets, and surveys to classify sentiment (positive, negative, neutral) and spots unusual patterns in data transformations. The service integrates with existing data infrastructure that uses dbt models and is sold through tiered subscriptions that include basic sentiment analysis, advanced anomaly detection, and premium support. Its goal is to help organizations make informed decisions and improve customer satisfaction by delivering continuous analytics enablement and actionable insights.

Company size
201-500
Company stage
Late Stage VC
Total funding
$2.2B
Headquarters
Seattle, WA
Founded
2021

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