ML Engineer, Trust & Safety: Scale Abuse Detection

Fdetimes; San Francisco (CA); Ibrido; USD 208.000 - 312.000; Tempo pieno

Vantaggi offerti da questo lavoro

Equity

Healthcare

Mentorship & events

Flexible time off

WFH budget

Descrizione del lavoro

Vercel is seeking a Machine Learning Engineer on the Trust & Safety Engineering team to build production systems that detect and stop abuse at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code and 20% modeling, based in San Francisco or New York City with three days a week in the office.

You will design, build, and operate ML infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks.

Competenze

• 5+ years of software engineering experience building production systems.
• Proficient in Python; knowledge of JavaScript/TypeScript or Go.
• Experience with ML infrastructure, training/serving pipelines, or evaluation tooling.

Mansioni

• Design, build, and operate production systems that detect and disrupt abusive behavior at scale, prioritizing reliability and observability.
• Develop and maintain ML infrastructure (training pipelines, feature pipelines, serving infrastructure, evaluation tooling).
• Ship LLM and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code.
• Own end-to-end systems, from model integration to deployment, monitoring, and iteration.
• Collaborate with security, product, and infra teams to translate abuse patterns into production solutions.

Conoscenze

Python

JavaScript/TypeScript

Go

ML infrastructure

LLMs in production

Descrizione del lavoro

Vercel is seeking a Machine Learning Engineer on the Trust & Safety Engineering team to build production systems that detect and stop abuse at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code and 20% modeling, based in San Francisco or New York City with three days a week in the office.

You will design, build, and operate ML infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks.