Apple is seeking a Senior Machine Learning Engineer to help build managed platform services at the intersection of ML, distributed systems, and production engineering. This role supports the Apple AI platform within Apple Services Engineering (ASE), enabling teams across Apple to build, train, optimize, and deploy AI systems at scale. Our focus is on the optimization and intelligence layer for frontier AI, delivering serverless capabilities that span the full AI lifecycle: data and feature engineering, embeddings and retrieval, model training and fine-tuning, inference optimization and routing, prompt optimization, evaluation, and governance.

We are looking for a machine learning engineer who is excited about building production ML systems and ML infrastructure in a highly scalable environment. You will contribute to distributed systems and large-scale data processing, work on model serving and inference optimization, and build APIs and services used by other engineers. Strong collaboration and communication skills are essential, as is the ability to navigate ambiguity in fast-moving areas. A BS, MS, or PhD in Computer Science or equivalent practical experience is required.

Preferred qualifications include experience with LLM inference optimization (batching, quantization, KV caching, tensor parallelism), experience with model serving frameworks (e.g., vLLM, TensorRT, Ray Serve), experience with embedding models and retrieval systems (including vector databases and retrieval evaluation), experience with fine-tuning and alignment workflows (SFT, DPO, LoRA, RLHF, RLVR, GRPO

Apple is seeking a Senior Machine Learning Engineer to help build managed platform services at the intersection of ML, distributed systems, and production engineering. This role supports the Apple AI platform within Apple Services Engineering (ASE), enabling teams across Apple to build, train, optimize, and deploy AI systems at scale. Our focus is on the optimization and intelligence layer for frontier AI, delivering serverless capabilities that span the full AI lifecycle: data and feature engineering, embeddings and retrieval, model training and fine-tuning, inference optimization and routing, prompt optimization, evaluation, and governance.

We are looking for a machine learning engineer who is excited about building production ML systems and ML infrastructure in a highly scalable environment. You will contribute to distributed systems and large-scale data processing, work on model serving and inference optimization, and build APIs and services used by other engineers. Strong collaboration and communication skills are essential, as is the ability to navigate ambiguity in fast-moving areas. A BS, MS, or PhD in Computer Science or equivalent practical experience is required.

Preferred qualifications include experience with LLM inference optimization (batching, quantization, KV caching, tensor parallelism), experience with model serving frameworks (e.g., vLLM, TensorRT, Ray Serve), experience with embedding models and retrieval systems (including vector databases and retrieval evaluation), experience with fine-tuning and alignment workflows (SFT, DPO, LoRA, RLHF, RLVR, GRPO