Machine Learning Engineer, Trust & Safety

Responsibilities

• Design, build, and operate production systems that detect and disrupt abusive behavior at internet scale.
• Build and maintain ML infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks.
• Ship LLM and classical ML approaches for abuse detection and classification.
• Own model systems end to end, including platform integration, deployment, monitoring, and iteration.
• Collaborate with security, product, and infrastructure teams to convert abuse patterns and detection logic into production systems.

Requirements

• At least 5 years of software engineering experience building and operating production systems at scale.
• Production programming experience in Python plus JavaScript/TypeScript or Go.
• Experience building and maintaining ML infrastructure such as training and serving pipelines, feature stores, or evaluation tooling.
• Experience integrating LLMs into production systems, including prompt engineering and evaluation.
• Preferred: experience building an open source ML tool or working in trust and safety, fraud, or abuse prevention.

Benefits

• Competitive compensation package including equity.
• Inclusive healthcare package.
• Mentorship and attendance at events supporting professional development and networking.
• Flexible time off.
• Company-provided work equipment and a work-from-home budget.
• Hybrid work arrangement in San Francisco or New York City with three days per week in the office.