6 hours ago
Responsibilities
- Build managed platform services for training, optimizing, serving, and deploying machine learning and AI systems at scale.
- Develop optimization and intelligence capabilities across data and feature engineering, embeddings and retrieval, model training and fine-tuning, inference optimization and routing, prompt optimization, evaluation, and governance.
- Build APIs and production services consumed by other engineers.
- Work on distributed systems, large-scale data processing, model serving, inference optimization, and ML pipeline engineering.
Requirements
- At least 3 years of experience building production ML systems or ML infrastructure.
- Strong programming skills in Python and/or Rust or Java.
- Understanding of end-to-end machine learning workflows from data preparation through training, evaluation, and deployment.
- Experience with distributed systems, large-scale data processing, model serving, inference optimization, or ML pipeline engineering.
- Experience building APIs and services for other engineers.
- BS, MS, or PhD in Computer Science, or equivalent practical experience.
- Preferred qualifications include LLM inference optimization, model serving frameworks, embedding and retrieval systems, fine-tuning and alignment workflows, feature engineering and serving platforms, distributed data processing, offline and online stores, Ray, Kubernetes, cloud GPU infrastructure, and ML governance or lineage systems.
Tech Stack
Amazon DynamoDBApache CassandraApache FlinkApache SparkAWSGoogle Cloud PlatformJavaKubernetesPythonRedisRust
Categories
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.