Machine Learning Engineer - LLM

Operations Advanced Analytics team is looking for creative and motivated hands on individual contributors who thrive in dynamic environment and enjoy working with multi-functional teams. As a member of our team, you will work on applied machine-learning algorithms to seek problems that focus on topics such as classification, regression, clustering, optimizations and other related algorithms to impact and optimize Apple s supply chain and manufacturing processes. As a part of this role, you would work with the team to build end to end machine learning systems and modules, and deploy the models to our factories. Youll be collaborating with Software Engineers, Machine Learning Engineers, Operations, and Hardware Engineering teams across the company.

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.

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.