Remote ML Engineer, Trust & Safety & Moderation

Ladders; United States; Remote; USD 137,000 - 182,000; Full time

Benefits offered by this job

Remote work

Innovative team

Generative AI exposure

Child safety focus

First-of-its-kind product

Job summary

Ladders is seeking an ML Engineer, Trust & Safety to join a leader in the Retail & Consumer Goods space. This role will shape and strengthen safety systems for a consumer-facing product in a fast-moving, highly visible environment.

You will own the moderation and safety workflow end to end, partnering with product, engineering, and operations stakeholders to help ensure a safe user experience. The work blends applied machine learning, evaluation design, and incident readiness, with a strong

Qualifications

• 5-7 years of applied safety engineering experience on user-facing products.
• Experience building evaluations or classifiers for harmful content.
• Strong proficiency in Python for production code development.
• Ability to navigate precision/recall trade-offs effectively.
• Demonstrated good judgment under pressure.

Responsibilities

• Own and maintain end-to-end content moderation from input to output.
• Develop and enhance safety evaluation suites for model readiness.
• Continuously improve classifiers and assess model integration.
• Tune moderation processes to minimize over-blocking while ensuring safety.
• Conduct ongoing red teaming against models and moderation pipelines.
• Maintain review tools for flagged content and user reports.
• Manage the incident response runbook for safety issues.
• Oversee compliance obligations related to data retention and deletion.

Skills

Python

Safety engineering

Evaluation design

Moderation judgement

Job description

Ladders is seeking an ML Engineer, Trust & Safety to join a leader in the Retail & Consumer Goods space. This role will shape and strengthen safety systems for a consumer-facing product in a fast-moving, highly visible environment.

You will own the moderation and safety workflow end to end, partnering with product, engineering, and operations stakeholders to help ensure a safe user experience. The work blends applied machine learning, evaluation design, and incident readiness, with a strong