a16z 
Sequoia Machine Learning Engineer Safety
Posted Updated 8
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Summary
Machine learning engineer at fal who owns safety and abuse-detection models and their production infrastructure — improving detection accuracy, coverage, latency, and scalability, integrating safety systems with the core platform, and evaluating third-party safety tooling. Must be based in San Francisco.
You will own machine learning models and production infrastructure for safety and abuse detection. You will improve detection accuracy, coverage, latency, and scalability; integrate systems with core infrastructure; assess third-party tools; and introduce relevant safety techniques.
Responsibilities
- Design, build, and maintain machine learning models and infrastructure for safety and abuse detection
- Improve the accuracy, coverage, latency, and scalability of detection pipelines
- Integrate safety systems with core platform infrastructure
- Evaluate and integrate third-party safety tooling and vendor models
- Apply new machine learning safety techniques and infrastructure patterns
Requirements
- Hands-on experience in trust and safety, content moderation, or abuse and detection systems
- End-to-end engineering ability across machine learning and production infrastructure
- Ability to own ambiguous, high-stakes problems
- Based in San Francisco
Benefits
- Equity
- Relocation assistance to San Francisco
- Health, dental, and vision insurance
- Regular team events and offsites