Senior ML Engineer: Large-Scale Data Infra

Apple, Inc.; Santa Clara (CA); In loco; USD 216.000 - 325.000; Tempo pieno

Vantaggi offerti da questo lavoro

Employee stock programs

Employee Stock Purchase Plan

Medical and dental coverage

Tuition reimbursement

Relocation assistance

Discretionary bonuses or commission

Descrizione del lavoro

Apple, Inc. in Santa Clara, California is hiring for a senior distributed systems role focused on building the storage and compute foundation for high-performance computing environments.

You will help design a petabyte-scale lakehouse and an in-house MapReduce-like framework that runs thousands of workers on Kubernetes. The role emphasizes scaling, reliability, and cost optimization across vast data stores, with on-call duties and collaboration across teams.

Competenze

• 5+ years experience designing, building, and operating large-scale distributed systems
• MS or PhD in Computer Science or related field
• Experience with petabyte-scale batch data processing (MapReduce, Spark, or similar)
• Proficiency in cloud-native architectures and compute orchestration on AWS and Kubernetes

Mansioni

• Design and build our web signal store, a petabyte-scale Parquet/Iceberg lakehouse.
• Design and build our in-house MapReduce-style processing framework on Kubernetes.
• Improve performance, reliability, and cost at trillion-row scale.
• Own production health, including monitoring, on-call, and root-cause fixes.

Conoscenze

Strong coding skills

Distributed systems

Petabyte-scale data processing

Cloud (AWS, Kubernetes)

Cross-team collaboration

MS or PhD in Computer Science

Formazione

MS or PhD in Computer Science

Strumenti

Spark

Hadoop

Kubernetes

AWS (S3, EC2, EKS)

Descrizione del lavoro

Apple, Inc. in Santa Clara, California is hiring for a senior distributed systems role focused on building the storage and compute foundation for high-performance computing environments.

You will help design a petabyte-scale lakehouse and an in-house MapReduce-like framework that runs thousands of workers on Kubernetes. The role emphasizes scaling, reliability, and cost optimization across vast data stores, with on-call duties and collaboration across teams.