Engineering Manager, Machine Learning (Safety)

Role overview Lead a machine learning team building systems that detect harmful content, account risks, and platform abuse. You’ll shape the technical direction and production lifecycle of real-time and batch safety models, balancing hands-on engineering with team development and close partnership across safety, product, policy, legal, and data science.

Responsibilities - Hire, coach, and support a highly engaged team of machine learning engineers, building ownership and focus on impact. - Set the team’s technical vision and roadmap with partner groups across safety, product, policy, legal, and data science. - Own production safety models end to end: define capabilities, measure performance, and monitor operational health. - Improve team processes and use technical judgment to support reliable delivery at scale. - Partner with safety teams on label quality, evaluation sets, and automation of manual investigations. - Collaborate with other engineering managers to improve engineering practices and the broader organization.

Requirements - At least five years of experience as a machine learning engineer, data scientist, or applied scientist. - At least three years managing engineering teams, including a team of five or more engineers. - Deep hands-on experience in at least one area such as abuse or fraud detection, content classification, behavioral or graph modeling, or LLM-based classification. - Proven experience shipping machine learning systems to production at scale. - Strong communication and cross-functional collaboration skills, with the ability to make progress in ambiguous problem spaces. - A pragmatic, first-principles approach to solving complex technical problems. - Interest in coaching engineers and willingness to contribute directly to code when needed. - Habit of tracking industry developments and assessing technologies that could address technical needs.