Senior Machine Learning Engineer, Apple Cloud AI

Summary

Apple is a place where extraordinary people gather to do their best work. Together we build products and experiences people love. The Apple Services Engineering (ASE) organization builds and operates the systems and infrastructure that power Apple's services at scale.

The Apple AI platform within ASE enables teams across Apple to build, train, optimize, and deploy AI systems at scale. Our team builds the optimization and intelligence layer for frontier AI, making frontier class of models work better, cheaper, and faster through managed, 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.

About the role

We are looking for an ML engineer who is excited about building managed platform services at the intersection of ML, distributed systems, and production engineering.

Responsibilities

As a member of the team, your responsibilities will include

• Design, build, and optimize large-scale ML platform services used by teams across Apple
• Build the embedding and retrieval path end to end - fine-tuning encoder models, encoding corpora at scale, building and serving vector indexes, and evaluating retrieval quality so improvements are measurable rather than asserted
• Build and operate the feature store teams use for training and serving, keeping both paths consistent off a single feature definition
• Develop optimization capabilities that reduce cost and improve quality across ML workloads - including model routing, caching, serving configuration, inference optimization, and training efficiency
• Build managed, self-service experiences so customers can go from data to production AI with minimal friction
• Build managed training - supervised fine-tuning, reinforcement learning and distillation - so teams can customize models without running their own training infrastructure
• Build governance and compliance capabilities - lineage, policy enforcement, cost observability, and access control
• Partner with customer teams across Apple to understand their ML workloads and deliver production solutions

Minimum Qualifications

• 3+ years of experience building production ML systems or ML infrastructure
• Strong programming skills in Python and/or Rust/Java
• Understanding of end-to-end machine learning workflows - from data preparation through training, evaluation, and deployment
• Experience with distributed systems and large-scale data processing

Experience with model serving, inference optimization, or ML pipeline engineering:

• Experience building APIs and services that other engineers consume
• Strong collaboration and communication skills
• Comfortable navigating ambiguity in fast-moving areas
• BS, MS, or PhD in Computer Science or equivalent practical experience

Preferred Qualifications

• Experience with LLM inference optimization (batching, quantization, KV caching, tensor parallelism)
• Experience with model serving frameworks (vLLM, TensorRT, Ray Serve, or similar)
• Experience with embedding models and retrieval systems - fine-tuning encoders on graded or contrastive objectives, pooling strategies, dimensionality reduction for serving cost, vector databases, and retrieval evaluation (NDCG, recall, graded relevance)
• Experience with fine-tuning and alignment workflows (SFT, DPO, LoRA, RLHF, RLVR, GRPO, reward modeling)
• Experience with feature engineering and feature serving platforms (e.g. Feast, Tecton, Hopsworks), distributed data processing frameworks (e.g. Spark, Flink, Ray), offline stores (e.g. Iceberg, Delta, Lance), and online stores (e.g. Redis, Cassandra, DynamoDB)
• Experience with Ray, Kubernetes, and cloud GPU infrastructure (AWS, GCP)
• Experience with ML governance, lineage, or compliance systems