What are the responsibilities and job description for the Machine Learning Engineer - LLM position at Apple?
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. The people here at Apple don’t just create products — they create the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts.
Join Apple, and help us leave the world better than we found it.
We are seeking a highly capable and dynamic Machine Learning Engineer to work cross-functional in building out a GenAI system in support of Beats and Apple Audio products. The ideal candidate thrives in ambiguity, is exceptionally organized, relentlessly detail-oriented, and an exceptional communicator at all levels of the organization.
Minimum Qualifications:
3 years experience in machine learning algorithms, software engineering, and data mining models with an emphasis on large language models (LLM) or large multimodal models (LMM).
Masters in Machine Learning, Artificial intelligence, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field.
Ability to travel internationally - up to 10%
Preferred Qualifications:
Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning.
Experience applying ML techniques in manufacturing, testing, or hardware optimization.
Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc.
Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale.
Hands-on experience with LangChain and LlamaIndex, enabling RAG applications and LLM orchestration.
Ability to meaningfully present results of analyses in a clear and impactful manner, breaking down complex ML/LLM concepts for non-technical audiences.
Proven experience in leading and mentoring teams.