ML Engineer, Ads Platform — Low Latency & Privacy

Apple; Cupertino (CA); On-site; USD 147,000 - 272,000; Full time

Benefits offered by this job

Medical/dental coverage

Retirement benefits

Employee stock programs

Tuition reimbursement

Job summary

Apple Ads is hiring a hands-on Machine Learning Engineer to design and build ML systems and data pipelines that safeguard advertiser trust and improve invalid traffic protections.

The role involves defining an innovation roadmap, deploying models with robust CI/CD, feature stores, and streaming infrastructure (e.g., Kafka/Spark/Flink), and driving performance tuning, calibration, and drift detection to deliver measurable improvements in product quality, user experience, and cost.

Qualifications

• 4+ years of machine learning experience across multiple product areas at scale.
• Proficiency in Java, Python, or Scala for algorithm and system development.
• Experience with distributed systems and big data frameworks (Spark, Kafka, Hadoop, Flink).
• Knowledge of CI/CD workflows, cloud environments, and containerized deployments.
• BS or MS in Computer Science, Software Engineering or related field.

Responsibilities

• Develop and manage end-to-end lifecycle of ML models for production systems.
• Design and optimize distributed algorithms and data processing frameworks (e.g., Spark).
• Implement scalable feature pipelines to ingest, clean, transform, and analyze massive datasets.
• Collaborate with product and engineering teams on production systems and applications.

Skills

Java

Python

Scala

Distributed systems

CI/CD

Cloud environments

Education

BS in CS

MS in CS

Tools

Spark

Kafka

Hadoop

Flink

Job description

Apple Ads is hiring a hands-on Machine Learning Engineer to design and build ML systems and data pipelines that safeguard advertiser trust and improve invalid traffic protections.

The role involves defining an innovation roadmap, deploying models with robust CI/CD, feature stores, and streaming infrastructure (e.g., Kafka/Spark/Flink), and driving performance tuning, calibration, and drift detection to deliver measurable improvements in product quality, user experience, and cost.