Sr Engineering Program Manager - Health AIML

Apple•Cupertino, CA

18h

About The Position

As part of Apple’s AI and Machine Learning org, you will be developing new ML-powered Health features and capabilities for users. This role will drive the platform used to build Health features as well as building and improving new features and products to make people healthier. You will program manage AI/ML Health platform and features.

Requirements

10+ years of experience in project or program management with prior experience in machine learning.
Deep Health domain expertise in AI based health solutions.
Excellent program management skills including program structuring and managing multiple work streams interdependently with the capability to work across a large number of teams to enforce process and efficient outcomes in a data-driven environment.
Strong product and technical orientation and ability to maintain sight of user experience during feature development.
Strong analytical skills and obsession for data, solid understanding of metrics measuring machine learning models and overall system quality.
Demonstrated data-driven decision-making skills, including experience leading metrics-driven projects or programs.
Strong verbal and written communication skills, including ability to independently draft and present deliverables, recommendations.
Demonstrated ability to drive decisions, resolve conflicts, and influence outcomes without direct authority.
BS, MS or PhD in a quantitative field (Statistics, Computer Science, etc.) or equivalent experience.
Prior PM or engineering experience in shipping products driven by machine-learned systems to worldwide users.

Nice To Haves

Familiarity with benchmarks used in Health technologies and products
MS or PhD in a quantitative field (Statistics, Computer Science, etc.) or equivalent experience.

Responsibilities

Define, plan, and execute complex AI/ML programs across multiple teams (engineering, data science, product, and clinical operations).
Drive alignment between technical roadmaps, business objectives, and regulatory requirements.
Manage project timelines, deliverables, and partner communication.
Partner with data scientists and ML engineers to design scalable model training, validation, and deployment pipelines.
Oversee MLOps practices—monitoring model performance, versioning, and retraining strategies.
Translate technical concepts into business language for non-technical partners.
Facilitate communication between clinicians, data scientists, and software engineers.
Identify and mitigate risks related to data quality, regulatory changes, and technical dependencies.
Define and track success metrics for AI/ML initiatives (e.g., accuracy, recall, clinical impact, time-to-deploy).
Use data-driven approaches to optimize delivery processes and product outcomes.