The Apple Ray team is looking for a Senior / Staff Software Engineer with expertise in distributed systems and machine learning. This hybrid role involves designing and building core components of Apple's unified data and ML platform using open-source Ray. The engineer will work closely with ML teams to ensure the platform supports large-scale training and inference workloads. The ideal candidate will have a strong foundation in low-level systems work and be fluent in ML workflows and models at scale.

• 6+ years of experience building distributed systems, high-scale backend services, or compute runtimes.
• Solid background in ML workflows, model training, model serving, or data pipeline development.
• Proficiency in Python and strong experience in a systems-level language such as C++, Rust, Go, or Java.
• Experience with ML frameworks like PyTorch or TensorFlow and familiarity with GPU-based training.
• Understanding of parallelism strategies, model scaling, or distributed training concepts.
• Experience with cluster orchestration tools like Kubernetes, EKS, or GKE.
• Strong debugging skills across distributed and ML-centric runtime environments.
• Ability to work cross-functionally with ML engineers, data engineers, and infrastructure teams.
• B.S., M.S., or Ph.D. in Computer Science, Machine Learning, or related technical fields, or comparable software engineering experience.
• Experience with distributed training frameworks such as DeepSpeed, Horovod, FSDP, or ZeRO.
• Background in optimizing GPU workloads or performance benchmarking.
• Experience with model orchestration systems or ML platforms.
• Contributions to open-source ML or distributed systems projects.
• Familiarity with large-scale data systems such as Spark, Flink, or similar.

• Design and build core components of Apple's unified data and ML platform powered by open-source Ray.
• Contribute to the distributed runtime, orchestration layer, and system APIs.
• Collaborate closely with ML practitioners to translate model and pipeline needs into robust platform capabilities.
• Enhance developer productivity for ML engineers and improve resource efficiency.
• Advance the performance and reliability of Apple's ML workloads.
• Improve the underlying distributed runtime and control plane.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.