AIML - Machine Learning Engineer, Foundation Models

Apple Inc.
Seattle, United States of America
4 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Seattle, United States of America

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Apple Products
Azure
Cloud Computing
Program Optimization
Computer Programming
Distributed Computing Environment
Python
Machine Learning
Open Source Technology
TensorFlow
Google Cloud Platform
PyTorch
Large Language Models
Deep Learning
Kubernetes
Information Technology
Machine Learning Operations
Artificial Intelligence Markup Language (AIML)
Docker

Job description

Apple is revolutionizing artificial intelligence by developing sophisticated foundation models that power intelligent features across our product ecosystem. We're seeking skilled Machine Learning Engineers to transform cutting-edge research into scalable, production-ready AI solutions. We are looking for engineers who are passionate about building systems that push the frontier of deep learning in terms of scaling, efficiency, and flexibility and delight millions of users in Apple products.

Requirements

  • MS or PhD in Computer Science, Machine Learning, or related technical field
  • Expert-level programming skills in Python
  • Proficiency in machine learning frameworks such as Jax, PyTorch, TensorFlow
  • Strong background in: Distributed training, Model optimization, and Machine learning infrastructure
  • Experience with large-scale model training and deployment
  • Familiarity with: Kubernetes, Docker, Cloud platforms (AWS, GCP, Azure)
  • Distributed computing frameworks

Preferred Qualifications

  • Experience with foundation models and large language models
  • Background in multi-modal AI systems
  • Demonstrated ability to transform research prototypes into production systems
  • Published research or significant contributions to open-source ML projects
  • Understanding of on-device machine learning techniques

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