Job Description

Locations

• Cupertino, California, United States 95014

Salary

Skills Required

• Machine Learning Engineeringintermediate
• designing real-time ML systemsintermediate
• building large-scale data pipelinesintermediate
• end-to-end prediction and decisioningintermediate
• personalizationintermediate
• retrieval/rankingintermediate
• allocationintermediate
• optimizationintermediate
• upholding reliability standardsintermediate
• upholding privacy standardsintermediate
• upholding safety standardsintermediate
• defining innovation roadmapintermediate
• productionizing modelsintermediate
• robust CI/CDintermediate
• feature storesintermediate
• streaming infrastructureintermediate
• Kafkaintermediate
• Sparkintermediate
• Flinkintermediate
• A/B experimentationintermediate
• performance tuningintermediate
• model calibrationintermediate
• drift detectionintermediate
• architectureintermediate
• monitoringintermediate
• SLAsintermediate
• recommendationsintermediate
• response predictionintermediate
• experimentation toolingintermediate

Required Qualifications

• 4+ years of experience building machine learning capabilities across many different product areas at scale (experience, 4 years)
• Strong proficiency in Java, Python, or Scala for algorithm and system development. (experience)
• Experience with distributed systems and big data frameworks such as Spark, Kafka, Hadoop, or Flink. (experience)
• Solid understanding of data structures, algorithms, and system design principles. (experience)
• Expertise in working with relational databases (PostgreSQL, MySQL, Oracle) and NoSQL/Cloud storage (S3, GCS, etc.). (experience)
• Familiarity with CI/CD workflows, cloud environments, and containerized deployments. (experience)
• Knowledge of data validation, cleansing, and quality assurance practices. (experience)
• Understanding of statistical methods, A/B testing, and online experimentation frameworks. (experience)
• Prior experience working with machine learning platforms or real-time recommendation engines is a plus. (experience)
• BS or MS in Computer Science, Software Engineering or related technical fields. (experience)

Preferred Qualifications

• 7+ years of experience building machine learning capabilities across many different product areas at scale. (experience, 7 years)
• Background in Advertising systems. (experience)
• Hands-on experience with service reliability engineering (SRE) and SLA monitoring. (experience)
• Contributions to open-source algorithm frameworks or data processing tools. (experience)

Responsibilities

• 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 decisioning—spanning personalization, retrieval/ranking, allocation, and optimization—while upholding strong reliability, privacy, and safety standards. You’ll 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.
• Design, develop, and optimize distributed algorithms and data processing frameworks (e.g., Spark).
• Implement scalable data pipelines to ingest, clean, transform, and analyze massive datasets.
• Collaborate with machine learning engineers to deploy and operationalize algorithms in production.
• Own the full lifecycle of services—from architecture to monitoring—for high-throughput, low-latency applications.
• Drive performance optimization, bottleneck analysis, and system tuning across compute and storage layers.
• Build tools to support A/B testing, statistical evaluation, and experimentation pipelines.
• Ensure data integrity, security, and compliance across all solutions.
• Participate in cross-functional Agile teams to prototype and deliver impactful, data-driven products.

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