Machine Learning Engineer, Trust & Safety

What you’ll do:

• 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.

What you need:

• 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.

Bonus if you:

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

Compensation & 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.
• The San Francisco, CA base pay range for this role is $208,000.00 - $312,000.00. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role.