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. 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. Youll 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).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 experiencePhD 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