Senior Engineering Manager, ML Infrastructure for Ads Safety

Responsibilities

• Lead and grow an organization of multiple engineers and managers, providing technical direction and career mentorship to scale the team’s impact.
• Oversee the strategy and execution of ML infrastructure, ensuring the platforms used for content and actor detection are robust, scalable, and efficient.
• Collaborate with cross-functional partners to align infrastructure capabilities with the evolving needs of machine learning models and safety enforcement.
• Drive the technical roadmap for the platform, balancing long-term architectural improvements with the immediate operational needs of the Ads Safety organization.
• Manage organizational health and operational excellence, establishing high standards for engineering practices and fostering a high-performing team culture.
• - Lead and grow an organization of multiple engineers and managers, providing technical direction and career mentorship to scale the team’s impact. - Oversee the strategy and execution of ML infrastructure, ensuring the platforms used for content and actor detection are robust, scalable, and efficient. - Collaborate with cross-functional partners to align infrastructure capabilities with the evolving needs of machine learning models and safety enforcement. - Drive the technical roadmap for the platform, balancing long-term architectural improvements with the immediate operational needs of the Ads Safety organization. - Manage organizational health and operational excellence, establishing high standards for engineering practices and fostering a high-performing team culture.

Minimum qualifications:

Preferred qualifications:

Qualifications

• Minimum qualifications: - Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience. - 8 years of technical leadership and people management experience, including in leading managers. - 8 years of experience with software development. - 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). - 5 years of experience building and developing large-scale infrastructure or distributed systems. Preferred qualifications: - Master’s degree or PhD in Engineering, Computer Science, or a related technical field. - 8 years of experience in software engineering with a focus on infrastructure systems or machine learning platforms. - 5 years of experience in technical leadership, including managing engineering managers and overseeing an organization of 15+ people. - 5 years of experience working in a complex organization. - Experience building and scaling ML infrastructure, specifically platforms for model inference, training, or data pipelining.