6 hours ago
Responsibilities
- Design and develop secure, scalable, high-performing backend systems for machine learning products.
- Build ML infrastructure, frameworks, and services used by multiple teams.
- Develop feature stores or comparable ML data infrastructure for production models.
- Manage embedding generation, versioning, refresh and retirement policies, and large-scale storage and retrieval.
- Build and deploy machine learning models across training, evaluation, serving, and inferencing workflows.
- Create multimodal training data pipelines spanning text, image, and structured data with sampling and point-in-time correctness.
- Build production batch and streaming ML systems using workflow orchestration and modern storage formats.
- Apply data modeling, testing, validation, automation, debugging, performance tuning, and reliability practices.
- Partner with technical and non-technical teams to identify projects and deliver ML platform capabilities.
Requirements
- Experience writing mission-critical production code for machine learning systems.
- Experience building ML infrastructure, frameworks, or services used by multiple teams.
- Experience with feature stores or comparable ML data infrastructure serving production models.
- Experience with embedding management and retrieval at scale.
- Working experience building and deploying models across the ML lifecycle.
- Understanding of model evaluation, train-serve skew, and data drift.
- Working knowledge of deep learning architectures and training frameworks such as PyTorch or TensorFlow.
- Experience applying ML at scale in advertising, recommender systems, information retrieval, or related domains.
- Experience generating training data from multimodal text, image, and structured data.
- Experience building production ML data pipelines using distributed processing systems.
- Experience with batch and streaming deployments, workflow orchestration, and modern storage formats.
- Strong data modeling, data architecture, system quality, reliability, testing, and validation skills.
- Strong problem-solving, debugging, and performance-tuning abilities.
- PhD in Computer Science or a related field plus 3+ years of engineering experience and 5+ years of machine learning experience; or MS plus 6+ years of engineering experience and 5+ years of machine learning experience; or BS plus 7+ years of engineering experience and 5+ years of machine learning experience.
- Preferred qualifications include advertising industry experience, LLM-based data generation or evaluation, large-scale distributed training, privacy-preserving ML, and agentic AI familiarity.
Tech Stack
PyTorchTensorFlow
Categories
Data EngineeringML Engineering
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.