Senior Machine Learning Engineer, Apple Cloud AI

Apple; Seattle (WA); On-site; USD 150,000 - 190,000; Full time

Job summary

Apple is seeking an ML Engineer to build and operate managed platform services at scale within the ASE organization. You will own end-to-end ML workflows, embedding and retrieval pipelines, feature stores, and governance across production AI workloads.

You will partner with teams across Apple to deploy production solutions, optimize costs, and ensure reliable on-call operations. This role demands strong Python/Rust/Java skills and extensive distributed systems experience.

Qualifications

• 3+ years building production ML systems or ML infrastructure.
• Strong programming skills in Python and/or Rust/Java.
• Understanding end-to-end ML workflows from data prep to deployment.
• Experience with distributed data processing and large-scale systems.
• Experience with model serving and inference optimization.
• Experience building APIs and services.
• Strong collaboration and communication skills.
• Comfortable navigating ambiguity in fast-moving areas.

Responsibilities

• Design, build, and optimize large-scale ML platform services used across Apple.
• Develop embedding and retrieval path end-to-end including vector indexes and quality evaluation.
• Build and operate the feature store used for training and serving.
• Improve ML workloads cost and quality via routing, caching, and configuration.
• Create self-service experiences enabling data-to-production AI.
• Support training with supervised fine-tuning and RL methods.
• Develop governance, lineage, and access control for ML workloads.
• Collaborate with customer teams across Apple to deliver production solutions.
• Operate production services with on-call responsibilities.

Skills

Production ML

Python/Rust/Java

ML workflows

Distributed systems

Model serving

APIs

Collaboration

Ambiguity

Education

BS/MS/PhD in CS or equivalent

Tools

vLLM

TensorRT

Ray Serve

Spark

Kubernetes

AWS/GCP

Job description

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.

Description

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.

Key Responsibilities

• 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
• Operate production services with on-call responsibilities

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