About the position
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.
Responsibilities
• Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability
• 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
Requirements
• 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
Nice-to-haves
• Built an open source ML tool
• Worked in the trust and safety, fraud, or abuse prevention domain
Benefits
• Competitive compensation package, including equity.
• Inclusive Healthcare Package.
• Learn and Grow - we provide mentorship and send you to events that help you build your network and skills.
• Flexible Time Off.
• We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed.
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