Machine Learning Engineer - AI Evaluation & LLM Systems

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. You’ll 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.

This role requires strong engineering fundamentals, a passion for machine

learning, and the curiosity to solve challenging problems at the intersection of

AI, data, and software engineering. If you’re excited about building the tools

that help define the future of AI quality at Apple, we’d love to hear from you.

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.

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.