Senior ML Scientist — Ads Optimization & Relevance

Socket.dev; Cupertino (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Apple is seeking a highly motivated engineer to design and build scalable machine learning and optimization capabilities for Apple Ads. You will help advertisers optimize campaigns, drive performance, and push state-of-the-art techniques into production at a global scale.

The role requires deep ML knowledge, strong programming in Java or Python, and experience with distributed frameworks like Spark/Hadoop. Collaboration with cross-functional teams under tight deadlines is essential.

Qualifications

• 5+ years building ML and quantitative optimization at scale.
• Experience in ML, quantitative methods, control systems, or reinforcement learning.
• Translate research concepts into production-quality code.
• Define testable hypotheses with impact on the business.
• Strong knowledge of online experimentation at scale.
• Present R&D results to cross-functional leadership and product teams.
• Contribute to research discussions for conferences/publications.
• Fluent in Java or Python.
• Experience with Spark, Hadoop or similar distributed frameworks.
• Masters/PhD with production ML experience or equivalent industry work.

Responsibilities

• Design and build scalable ML solutions for Apple Ads to optimize campaigns.
• Develop budget and bid optimization features to improve performance.
• Move state-of-the-art techniques into production at global scale.
• Collaborate with cross-functional teams under tight deadlines.

Skills

Machine learning

Optimization

Data analysis

Java

Python

Education

Master's degree in ML/Statistics/Control

PhD in ML/Statistics/Optimization

Tools

Spark

Hadoop

Job description

Apple is seeking a highly motivated engineer to design and build scalable machine learning and optimization capabilities for Apple Ads. You will help advertisers optimize campaigns, drive performance, and push state-of-the-art techniques into production at a global scale.

The role requires deep ML knowledge, strong programming in Java or Python, and experience with distributed frameworks like Spark/Hadoop. Collaboration with cross-functional teams under tight deadlines is essential.