Machine Learning Engineer, Trust & Safety

The shape of it

In the posting’s own words

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.

What it asks for · 1

• 5+ years of software engineering experience building and operating production systems at scale

Also a plus

• Built an open source ML tool
• Worked in the trust and safety, fraud, or abuse prevention domain

What the job covers

• 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

Tools and skills named

Words the posting leans on

• infrastructure5×
• production systems5×
• abuse4×
• pipelines4×
• build3×
• detection3×
• engineering3×
• evaluation3×
• platform3×
• status3×
• approaches2×
• built2×
• classification2×
• detect2×
• end2×
• llm2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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