LLM Machine Learning Engineer, Models and Agent Science, AIML

The Apple Intelligence Agents, Infrastructure, and Research team brings innovative AI research into Apple products, with a focus on optimizing, interpreting, and developing new algorithms for on-device and server-based Apple Foundation Models and Apple Intelligence features.

Description

We are looking for talented Machine Learning Applied Scientists and Research Engineers to build groundbreaking machine learning capabilities and drive emerging innovations. You will join a collaborative team of software developers and deep learning experts focused on large language modeling, optimization, interpretability, and related algorithms. In this role, you will drive applied innovation and evaluate emerging research for real-world viability, translating promising ideas into the Apple product context.

You'll bridge the gap between cutting-edge ideas and the constraints of shipping AI at scale.

Successful candidates will bring a strong software engineering background, hands-on zero-to-one machine learning development experience, and broad expertise in post-training machine learning models (including quality and performance optimization).

Minimum Qualifications

Proven ability to define goals and deliver results amid uncertainty and real-world constraints in AI product development

Ability to read, evaluate, and reproduce recent research and assess its practical viability under real-world constraints

Experience optimizing or post-training large language models (LLMs), developing interpretability or stress-testing algorithms, steering model behavior, or building agent harnesses

Strong Python and UNIX skills and a demonstrated ability to use agentic coding tools in these environments

History of applied research in neural network optimization, model training, or a related area

Proven track record of driving scientific investigations and experiments while overcoming obstacles and uncertainty in a research environment

BS and 5+ years of experience, MS and 3+ years of experience, or PhD and 1+ year of experience

Preferred Qualifications

PhD in a related field

Publication record at top AI/ML venues

Experience with post-training LLMs and network optimization algorithms, as well as interpretability or steering techniques for LLMs

Experience of working with large-scale compute infrastructure

Experience shipping a real world product, project or feature

Experimental rigor and ablation design when benchmarking LLM optimizations

Strong communication and accountability skills, with a collaborative mindset and strong work ethic