Apple Ray leverages open-source Ray to offer a unified framework for processing and deployment of complex dataML pipelines. It enables the next generation of intelligent experiences for Apple products and services by combining data and processing layers as well as a model inference platform into one unified end-to-end workflow that eliminates the complexity of running multiple independent jobs while significantly improving the hardware resource efficiency and development speed. Tight integration of Apple Ray with Apple Data services makes it the go-to solution when serving complex and large-scale data and ML pipelines. The team enables future Apple intelligent products by making a cutting edge ecosystem of dataML technologies for large-scale and efficient systems for all data and ML engineers within Apple. As a member of the Apple Ray team your responsibilities will include:* Designing implementing and maintaining distributed systems to build world-class ML platforms/products at scale* Experiment with deploy and manage LLMs in a production context* Benchmark and optimize inference deployments for different workloads e.g. online vs. batch vs. streaming workloads* Diagnose fix improve and automate complex issues across the entire stack to ensure maximum uptime and performance * Design and extend services to improve functionality and reliability of the platform* Monitor system performance optimize for cost and efficiency and resolve any issues that arise* Build relationships with stakeholders across the organization to better understand internal customer needs and enhance our product better for end users
5 years of experience in distributed systems with deep knowledge in computer science fundamentals
Experience managing deployments of LLMs at scale
Experience with inference runtimes/engines e.g. ONNXRT TensorRT vLLM sglang
Experience with ML Training/Inference profiling and optimization for different workloads and tasks e.g. online inference batch inference streaming inference
Experience with profiling ML models for different end use cases e.g. RAG vs. code completion etc.
Experience with containerization and orchestration technologies such as Docker and Kubernetes.
Experience in delivering data and machine learning infrastructure in production environments
Experience configuring deploying and troubleshooting large scale production environments
Experience in designing building and maintaining scalable highly available systems that prioritize ease of use
Experience with alerting monitoring and remediation automation in a large scale distributed environment
Extensive programming experience in Java Python or Go
Strong collaboration and communication (verbal and written) skills
B.S. M.S. or Ph.D. in Computer Science Computer Engineering or equivalent practical experience
Understanding of the ML lifecycle and state of the art ML Infrastructure technologies
Familiarity with CUDA kernel implementation
Experience with inference optimization and fine-tuning techniques (e.g. pruning distilling quantization)
Experience with deploying optimizing ML models on heterogenous hardware e.g. GPUs TPUs Inferentia etc.
Experience with GPU and other type of HPC infrastructure
Experience with training framework like PyTorch Tensorflow JAX
Deep understanding of Ray and KubeRay
At Apple base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181100 and $318400 and your base pay will depend on your skills qualifications experience and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apples discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards and can purchase Apple stock at a discount if voluntarily participating in Apples Employee Stock Purchase Plan. Youll also receive benefits including: Comprehensive medical and dental coverage retirement benefits a range of discounted products and free services and for formal education related to advancing your career at Apple reimbursement for certain educational expenses including tuition. Additionally this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.
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