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machine learning mlops multimodal policy_enforcement📋 Description
- Build and scale ML systems for proactive content detection and pre-publish safety scanning
- Design policy evaluation frameworks with datasets, offline/online metrics, and continuous improvement
- Develop multimodal models (text, audio, image, video) for safety enforcement
- Architect feedback loops turning reviewer input into training data
- Translate regulatory requirements into scalable ML designs
- Partner with Trust & Safety, Legal, Public Affairs, and Product to deliver safe experiences
- Drive technical direction in ambiguous spaces and contribute to platform architecture
- Mentor and support other ML engineers across the team
🎯 Requirements
- Experience building and shipping production-grade ML systems at scale
- Strong expertise in ML evaluation: datasets, metrics, and monitoring
- Experience with multimodal ML across text, audio, image, or video
- Experience with human-in-the-loop systems, active learning, or feedback-driven ML
- Translate complex regulatory/policy requirements into technical solutions
- Experience across teams and guiding technical direction in large-scale systems
- Navigate ambiguity and balance speed, quality, and risk
- Clear communication with technical and non-technical stakeholders
🎁 Benefits
- Health insurance
- Six-month paid parental leave
- 401(k) retirement plan
- Monthly meal allowance
- 23 paid days off
- 13 paid flexible holidays
- Paid sick leave
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