Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless.
Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here.
The Customer Support AI team is responsible for building multi-turn, conversational, agentic applications and frameworks to support Apple customers across numerous lines of business. You’ll be contributing hands on to a team that consists of engineers, data scientists & researchers to enhance a multi-modal, multi-agent platform with a key focus on incorporating research to improve, latency, cost and customer experience. This is an incredible opportunity to contribute innovation & research to a well established generative AI platform within Apple. There is a huge amount of opportunity and growth within this space!
Minimum Qualifications
5+ years of hands-on experience in ML, backend engineering, data engineering
1-2 years of hands-on experience in training, fine-tuning, or evaluating LLMs
Foundational understanding of RAG architectures and vector-based retrieval systems
Strong experience partnering with business and engineering team to deliver AI solutions
Bachelor's or Master's degree in Computer Science, Machine Learning, or related field, or equivalent practical experience.
Preferred Qualifications
Proficiency in machine learning libraries (transformers, datasets).
Strong problem-solving and collaboration skills, with the ability to learn quickly and adapt to production-grade systems.
Experience working with Multi-modal LLMs to enable Voice capabilities is a plus or prior experience with STT, TTS systems.
Experience with deploying to cloud environments (AWS, GCP, on-remote hybrid) is required.