The Apple Ads team is seeking a strategic hands-on Machine Learning Engineer to drive innovation across a modern large-scale platform. You will design build and operate real-time ML systems and large-scale data pipelines that power end-to-end prediction and decisioningspanning personalization retrieval/ranking allocation and optimizationwhile upholding strong reliability privacy and safety standards. Youll define and execute an innovation roadmap; productionize models with robust CI/CD feature stores and streaming infrastructure (e.g. Kafka/Spark/Flink); and run A/B experimentation. You will lead performance tuning calibration and drift detection to deliver measurable improvements in product quality user experience latency and cost. This role rewards ownership from architecture through monitoring and SLAs with influence across adjacent areas such as recommendations response prediction and experimentation tooling.
4 years of experience building machine learning capabilities across many different product areas at scale
Strong proficiency in Java Python or Scala for algorithm and system development.
Experience with distributed systems and big data frameworks such as Spark Kafka Hadoop or Flink.
Solid understanding of data structures algorithms and system design principles.
Expertise in working with relational databases (PostgreSQL MySQL Oracle) and NoSQL/Cloud storage (S3 GCS etc.).
Familiarity with CI/CD workflows cloud environments and containerized deployments.
Knowledge of data validation cleansing and quality assurance practices.
Understanding of statistical methods A/B testing and online experimentation frameworks.
Prior experience working with machine learning platforms or real-time recommendation engines is a plus.
BS or MS in Computer Science Software Engineering or related technical fields.
7 years of experience building machine learning capabilities across many different product areas at scale.
Background in Advertising systems.
Hands-on experience with service reliability engineering (SRE) and SLA monitoring.
Contributions to open-source algorithm frameworks or data processing tools.
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