Machine Learning Engineer - AI Evaluation & LLM Systems

DESCRIPTION

As a Machine Learning Engineer, you will build the systems that measure and improve the quality of AI experiences used by millions of people. You will develop machine learning models, evaluation frameworks, and scalable infrastructure that enable teams to understand model behavior, identify regressions, and accelerate the development of large language models and multimodal AI. Working closely with researchers, software engineers, and product teams, you will transform cutting-edge research into production-ready solutions, analyze large-scale datasets, and develop new approaches for benchmarking and improving AI quality.

This is a unique opportunity to solve challenging technical problems at the intersection of machine learning, software engineering, and data, while helping shape the future of AI at Apple.

MS, or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related technical field, or equivalent practical experience. 1–2 years of industry experience, or equivalent academic or internship experience, developing machine learning or AI solutions. Proficiency in Python and familiarity with C++ or another object-oriented programming language. Experience with one or more machine learning frameworks such as PyTorch, Tensor Flow, or JAX.

Understanding of machine learning fundamentals, including supervised learning, model evaluation, and statistical analysis. Experience working with data processing, model training, or experimentation through coursework, research, internships, or industry projects. Strong analytical, problem-solving, and communication skills with the ability to collaborate effectively in a team environment.

Experience with large language models (LLMs), multimodal AI, or generative AI through internships, research, or personal projects. Experience building software or machine learning projects using modern engineering practices (Git, testing, CI/CD). Familiarity with distributed computing, cloud platforms, or large-scale data processing. Publications, open-source contributions, or participation in machine learning competitions. MS or PhD specializing in Machine Learning, Artificial Intelligence, or a related field.