Senior Software Engineer, Apple Batch, Apple Data Platform

Apple • Cupertino, CA

1d

About The Position

Imagine what you could do here. At Apple, great ideas have a way of becoming
phenomenal products, services, and customer experiences very quickly. Bring
passion and dedication to your job and there's no telling what you could
accomplish. Do you love solving complex distributed systems challenges at
massive scale? Are you passionate about Kubernetes scheduling, resource
management, and building platforms that power the next generation of Machine
Learning and Data workloads? Do you thrive in designing and operating highly
reliable, large-scale job scheduling and orchestration systems that serve as the
backbone of AI and Data infrastructure? If so, join the Apple Data Platform team
to design and build a scalable batch and ML infrastructure platform used across
Apple. As part of Apple Data Platform, you will play a meaningful role in
designing, developing, and deploying high-performance systems that power batch
and ML workloads across Apple's global infrastructure spanning public clouds and
Apple data centers. This enormous scale brings unique and complex challenges in
resource scheduling, workload orchestration, and operational excellence that
require extraordinarily creative problem-solving.

DESCRIPTION

Apple Batch is a fully managed platform within the Apple Data Platform that
supports large-scale batch and ML workloads across Apple data centers and
AWS/GCP. It orchestrates containerized workloads such as Spark, Ray, and LLM
batch inference using YuniKorn/Kueue for advanced multi-cluster scheduling. The
platform delivers org/team quota management, automatic node repair, end-to-end
observability, strong security, and granular cost reporting. As part of the
Apple Batch team, you will have a meaningful role in designing, developing, and
deploying high-performance systems that power large-scale batch processing and
ML workloads daily. We are building critical infrastructure that provides
scalable batch execution, intelligent Kubernetes-native job scheduling,
multi-tenant resource management, and efficient workload orchestration for ML
training, inference, and data processing workloads across multi-cloud and
on-premises environments. We are looking for a strong, enthusiastic engineer
with deep expertise in Kubernetes scheduling and distributed systems. You will
have significant individual responsibility and influence over critical platform
services. You are someone with ideas and a real passion for building
infrastructure that improves reliability, efficiency, and simplicity at Apple
scale.

Requirements

• 5+ years of experience designing, developing, and operating highly available, large-scale distributed systems and data or ML infrastructure
• Strong software engineering skills with deep programming expertise in Go, Java, or Python
• Advanced knowledge of Kubernetes internals including custom controllers, scheduler architecture, resource quotas, and workload lifecycle management
• Hands-on experience with Kubernetes-native batch scheduling frameworks such as Kueue or YuniKorn and advanced scheduling concepts like gang scheduling, bin-packing, and priority preemption
• Experience with cloud-native infrastructure across multi-cloud environments including AWS, GCP, and on-premises systems
• Strong commitment to operational excellence, system observability, and continuous improvement for mission-critical services

Nice To Haves

• GPU scheduling, accelerator-aware placement, and optimization for large-scale AI/ML workloads
• Experience with distributed data and ML frameworks such as Apache Spark, Ray, PyTorch, JAX, or Flink at scale
• Experience contributing to open-source projects in Kubernetes scheduling, container technologies, or ML infrastructure ecosystems such as Apache YuniKorn, Kueue, or similar systems
• Experience using GenAI technologies to improve developer productivity, streamline engineering processes, and accelerate team execution

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees