Engineering Program Manager, Responsible AI, Safety

Apple•Cupertino, CA

About The Position

Apple’s AIML team is seeking an Engineering Program Manager to support and implement its strategy for the ethical and safe deployment of machine learning models. The role collaborates with cross-functional teams to develop and implement policies, standards, and processes aligned with Apple’s values, and to establish a safety lifecycle plan for machine learning models. The program manager will help build a culture of technology responsibility, encourage transparency, and mitigate risks associated with model development. The position coordinates and drives early engagement on product architecture, feature definition, and program execution, with a focus on Responsible AI safety. It works with relevant partner organizations to communicate program status and critical issues, facilitate discussions and engineering and design alignment, manage timelines and dependencies, assess and mitigate risks, drive delivery and deployment, and proactively resolve issues affecting schedule or product quality.

Requirements

At least 6 years of project or program management experience shipping products driven by machine-learned systems to worldwide users.
Experience designing delivery of multiple significant client/server software products requiring large organizations of 100 or more people.
Project management skills, including structuring projects and managing multiple interdependent work streams.
Detailed understanding of testing, build, and release processes for sophisticated software.
Demonstrated ability to negotiate and distill complex technical requirements from multiple partners into a coherent long-term platform roadmap.
Demonstrated data-driven decision-making skills, including experience leading metrics-driven projects or programs.
Experience in group facilitation, interviewing, and influence.
Ability to communicate abstract ideas clearly and independently manage complex project objectives.
Strong verbal and written communication skills, including negotiation, presentation, and influence.
Ability to independently draft and present deliverables, recommendations, timelines, and communication strategies.
BS, MS, or PhD in computer science, engineering, or another technical area, or experience in a related field.

Nice To Haves

Self-motivated and proactive, with creative problem-solving, critical thinking, and intellectual curiosity.
Prior hands-on software development experience.
Excellent collaboration skills, ability to work with different teams, and ability to foster trust.
Ability to thrive in a fast-paced, dynamic work environment.
Curiosity and passion for new technologies, with a proven ability to quickly understand technical concepts.

Responsibilities

Coordinate and drive early engagement on product architecture, feature definition, and program execution, focusing on Responsible AI safety.
Collaborate with partner organizations across projects.
Provide clear, timely, objective communication, including regular status updates and critical issues to the executive team.
Coordinate discussions and break complex issues into topics that enable engineering and design alignment, supporting teamwork and collaboration.
Create and manage project timelines, dependencies, critical paths, and methods for communicating status.
Manage risks and mitigations, re-plan as needed, and drive alignment across teams and the organization.
Drive on-time delivery and deployment by identifying development and feasibility needs and establishing checkpoints and status updates.
Proactively identify issues and solutions and marshal resources to resolve them.
Coordinate with teams to identify issues, prioritize tasks, and track risks to schedule and product quality, including cross-functional dependencies.
Support and implement AIML’s strategy for the ethical and safe deployment of machine learning models.
Collaborate on policies, standards, processes, and a safety lifecycle plan for machine learning models.
Help foster a culture of technology responsibility, encourage transparency, and mitigate model-development risks.