Personalization ML Engineer for Global App Store

Apple; Seattle (WA); On-site; USD 150,000 - 210,000; Full time

Job summary

Apple’s Services Engineering team is looking for a Machine Learning Engineer to design, build, and ship recommendation models powering personalization across App Store and other media surfaces.

You will partner with researchers and engineers to deploy models at scale for billions of users, with focus on low latency, reliability, and privacy—delivering production-ready code in a fast-paced environment.

Qualifications

• BS and MS in a quantitative field, such as CS, math, stats, or physics
• 3+ years of relevant work experience
• Hands-on experience with production-level recommender systems
• Strong knowledge of recommendation architectures and deep-learning techniques
• Experience shipping models to production at scale for billions of users
• Familiarity with ML tooling: TensorFlow, PyTorch, scikit-learn
• Familiarity with distributed compute and big data (Spark, Hadoop, Kafka)
• Excellent written and verbal communication

Responsibilities

• Design, train, and launch recommendation models to improve relevance and business metrics
• Own ranker modules end-to-end: representation, features, architecture, offline eval, A/B testing
• Advance production-ready ranking techniques from research to production
• Collaborate with researchers, data engineers, and infra teams to deploy models
• Ship production-quality code and uphold engineering best practices
• Help chart future personalization growth across Apple services

Skills

Recommender systems

Production deployment

Deep learning architectures

Multi-task modeling

Python ML stack (TF PyTorch)

Big data tech (Spark Hadoop Kafka)

Communication skills

Education

Bachelor's & Master's in quantitative field

Tools

TensorFlow

PyTorch

scikit-learn

NumPy/SciPy/Pandas

Job description

Apple’s Services Engineering team is looking for a Machine Learning Engineer to design, build, and ship recommendation models powering personalization across App Store and other media surfaces.

You will partner with researchers and engineers to deploy models at scale for billions of users, with focus on low latency, reliability, and privacy—delivering production-ready code in a fast-paced environment.