Senior ML Engineer

Seattle, Washington, United States Software and Services

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.

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 Yuni Korn/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.

• Design, build, and deploy highly reliable, large-scale distributed systems for batch processing and ML infrastructure across public clouds and Apple data centers using Go, Java, or Python
• Architect and operate Kubernetes-native scheduling systems such as Kueue and Yuni Korn, building custom operators and CRDs to manage complex ML and data workloads
• Implement advanced scheduling strategies including gang scheduling, topology-aware routing, bin-packing, and fair-share queuing to maximize GPU efficiency and hardware utilization
• Build and manage secure, multi-tenant Kubernetes environments with strict resource isolation, quota governance, and priority-based preemption
• Drive end-to-end observability, monitoring, and incident response practices to ensure high availability and fault tolerance of production systems
• Collaborate with ML researchers, data engineers, SRE, and product teams to integrate scheduling solutions into Apple's broader AI and data platform ecosystem
• Contribute to platform adoption by guiding internal customers, gathering requirements, and delivering impactful platform capabilities

• 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 Yuni Korn 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

• 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 Yuni Korn, Kueue, or similar systems
• Experience using GenAI technologies to improve developer…