LLM Machine Learning Engineer at Apple

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

29 May, 26

Salary

0.0

Posted On

28 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Large Language Models, Large Multimodal Models, Statistics, Data Mining, Algorithm Design, Deployment, Statistical Analysis, Business Intelligence, Python, PyTorch, TensorFlow, Hugging Face Transformers, Scikit-learn, Transformer Architectures, Inference Frameworks

Industry

Computers and Electronics Manufacturing

Description

Imagine what you could do here. At Apple, we believe new insights have a way of becoming excellent 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. It takes deeply dedicated, intelligent individuals to maintain and exceed the high expectations at Apple. The Product Operations team is looking for an extraordinary engineer to join our team. You will help design and implement our machine learning strategy to the substantial supply chain and help build the future of our manufacturing systems and smarter factories. We will be collaborating and working with multi-functional teams and applying algorithms to large-scale data. DESCRIPTION Product Operations partners with a variety of different engineering and operations teams, our team leads development of machine learning solutions. We deliver projects from end-to-end: problem statement and conceptualization, proof-of-concept, and participation in final deployment! You will also perform ad-hoc statistical analyses. You will also work closely with data engineers to generate detailed business intelligence solutions. You will be expected to conduct presentations of analyses to a wide range of audiences including executives. MINIMUM QUALIFICATIONS 3+ years of experience in machine learning algorithms, statistics, and data mining models, with an emphasis on large language models (LLM) or large multimodal models (LMM). Master’s degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering, or a related field. PREFERRED QUALIFICATIONS Proven experience in LLM and LMM development, fine-tuning, and application building. Experience with agents and agentic workflows is a major plus. Experience with modern LLM serving and inference frameworks, including vLLM for efficient model inference and serving. Hands-on experience with LangChain and LlamaIndex, enabling RAG applications and LLM orchestration. Strong software development skills with proficiency in Python. Experienced user of ML and data science libraries such as PyTorch, TensorFlow, Hugging Face Transformers, and scikit-learn. 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 is a plus. Deep understanding of transformer-based architectures (e.g., BERT, GPT, LLaMA) and their optimization for low-latency inference. Ability to meaningfully present results of analyses in a clear and impactful manner, breaking down complex ML/LLM concepts for non-technical audiences. Experience applying ML techniques in manufacturing, testing, or hardware optimization is a major plus. Proven experience in leading and mentoring teams is a plus.

Responsibilities

The engineer will design and implement machine learning strategy for the supply chain, focusing on building future manufacturing systems and smarter factories by applying algorithms to large-scale data. This involves leading end-to-end ML solution development, from conceptualization to deployment, and performing statistical analyses while collaborating with data engineers.