Machine Learning Engineering Manager - TikTok Community Health

The TikTok-BRIC-Community Health is responsible for addressing ecosystem risks in TikTok content creation, distribution, and social interactions, with a focus on combating underground industries facilitated by fake accounts, large-scale coordinated groups, and content or behaviors that harm community health and experience. We are looking for an experienced Machine Learning Engineering Manager to lead the US site of the team, responsible for combating fake account registration and protecting the integrity of social engagement.

You will be at the forefront of protecting our community, tackling unique and evolving challenges in a dynamic global environment. This role offers a significant opportunity to influence company strategy and build innovative solutions to ensure a safe and positive user experience.

Responsibilities
- Lead the technical direction and strategy for the team, focusing on community health and user safety
- Develop, deploy, and maintain sophisticated machine learning models and strategies to detect and mitigate risks such as fake accounts and inauthentic engagement.
- Architect and build robust, scalable systems for real-time risk detection and enforcement.
- Manage the day-to-day operations of the machine learning engineering team, fostering a culture of technical excellence, innovation, and continuous improvement
- Partner closely with Product Managers, Data Scientists, and Operations teams to define the technical roadmap, set clear milestones, and ensure the timely delivery of key initiatives
- Mentor and grow a team of talented engineers, providing technical guidance and career development support
- Communicate complex technical concepts and project outcomes to both technical and non-technical stakeholders

Minimum Qualifications:
- Proven experience as a manager with a strong track record of leading, mentoring, and growing a Machine Learning Engineering (MLE) team.
- Solid foundation in machine learning theory and practice, with demonstrated experience in areas like deep learning, NLP, graph-based learning, or large-scale classification systems.
- Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional, fast-paced environment.

Preferred Qualifications:
- Experience in the Trust & Safety, content moderation, anti-spam, or risk control domain.
- Hands-on experience building detection systems for fake accounts, coordinated networks (e.g., using graph neural networks), or harmful content at scale.
- Solid software engineering skills with proficiency in Python, SQL, and distributed computing frameworks (e.g., Spark, Flink, Hive).