Machine Learning Engineer, Trust & Safety

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We are looking for a Machine Learning Engineer on our Trust & Safety Engineering team, to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure...

Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability

We are looking for a Machine Learning Engineer on our Trust & Safety Engineering team, to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure) and 20% modeling, taking detection and classification approaches from prototype to hardened systems. This is a hybrid role based in San Francisco or New York City, with three days a week in the office.
Build and maintain machine learning (ML) infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks
Ship large language model (LLM) and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code
Own systems end to end, from integrating a model into the platform through deployment, monitoring, and iteration
Work with security, product, and infrastructure teams to turn abuse patterns and detection logic into production systems
5+ years of software engineering experience building and operating production systems at scale
Wrote production code in Python, plus JavaScript/TypeScript or Go
Built and maintained ML infrastructure, such as training and serving pipelines, feature stores, or evaluation tooling
Integrated LLMs into production systems, including prompt engineering and evaluation
Built an open source ML tool
Worked in the trust and safety, fraud, or abuse prevention domain