Machine Learning Engineer - AI Evaluation & LLM Systems

About the role

Join the team building the evaluation systems that enable Apple's next generation of AI experiences. As a Machine Learning Engineer, you will develop scalable infrastructure, intelligent evaluators, and data-driven methodologies that measure and improve the quality of large language models and multimodal AI systems used across Apple products.

Responsibilities

• Build systems that measure and improve the quality of AI experiences used by millions of people
• 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
• Partner closely with ML researchers, software engineers, and product teams to design novel evaluation techniques
• Analyze model behavior and translate research into production-ready systems
• Analyze large-scale datasets and develop new approaches for benchmarking and improving AI quality
• Transform cutting-edge research into production-ready solutions

Minimum qualifications

• 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, TensorFlow, 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

Preferred qualifications

• 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