Staff Machine Learning Engineer - Policy & Safety

What You Will Do

• Build and scale machine learning systems for proactive content detection, classification, and pre-publish safety scanning

• Design and implement policy evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops

• Develop multimodal models that combine text, audio, image, and video signals for safety and policy enforcement

• Architect feedback loops that turn human reviewer input into structured training data for continuous model improvement

• Translate regulatory requirements (e.g., precision/recall obligations, compliance reporting) into scalable ML system designs

• Partner with cross-functional teams across Trust & Safety, Legal, Public Affairs, and Product to deliver safe user experiences

• Drive technical direction in ambiguous problem spaces and contribute to long-term platform architecture

• Mentor and support other machine learning engineers, helping raise the bar across the team

Who You Are

• You have experience building and shipping production-grade machine learning systems at scale

• You have strong expertise in ML evaluation, including dataset design, metrics, and model performance monitoring

• You have worked with multimodal machine learning systems across text, audio, image, or video domains

• You are experienced with human-in-the-loop systems, active learning, or feedback-driven model improvement

• You are comfortable translating complex requirements into technical solutions, including regulatory or policy constraints

• You have experience working across teams and influencing technical direction in large-scale systems

• You are comfortable navigating ambiguity and making thoughtful decisions that balance speed, quality, and risk

• You communicate clearly and collaborate effectively with both technical and non-technical stakeholders

Where You Will Be

• This role is based in New York, NY

• We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.