Machine Learning Engineer - Multiple Teams

Summary

Imagine what you could do here. At Apple, revolutionary ideas have a way of becoming extraordinary products, services, and customer experiences. Join the Ai Data Platform Applied Machine Learning team to pioneer enterprise solutions where generative AI meets Apple's unique commitment to privacy-first innovation. Together, we'll create tools that redefine industries while safeguarding what matters most – our users' trust.

Minimum Qualifications

2+ years of hands-on experience in applied AI/machine learning work in industry or 4+ years of hands on AI research and development experience in academia Demonstrated expertise in generative AI, computer vision, natural language processing, or general machine learning with a passion for problem solving Bachelor of Science in Computer Science, Machine Learning or equivalent experience work experience

Preferred Qualifications

MS or Phd in Machine Learning, Natural Language Processing, Computer Vision or related areas strongly preferred Experience in ML frameworks for training, fine-tuning, and deploying ML/generative models at scale Proven track record of building large scale, enterprise-grade ML/Gen-AI products in cloud environments (AWS, GCP , Azure) or on-prem infrastructure

Description

As a pivotal member of Apple's enterprise generative AI efforts, you will help design, build, and evolve models, tools and applications that power high-impact AI experiences across the company. You will contribute to the architecture and optimization of AI/gen AI systems built for high availability, scalability, and reliability, working across backend services and application layers.

Responsibilities

You would solve AI problems in gen AI Safety, machine translation, content understanding, multi-modality, multi-agent systems, fine tuning and more. Our team designs and implements SOTA AI Models, services, and AI platform components that advance adoption of gen AI at apple. We tackle unique AI challenges in AI Safety, privacy-preserving generations, efficient inference, and multimodal integration, while enabling teams to build on top of our foundations. We deliver production-grade systems and models that meet Apple's rigorous standards for quality, performance, and scalability.