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 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 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 developmentnAbility to read evaluate and reproduce recent research and assess its practical viability under real-world constraintsnExperience optimizing or post-training large language models (LLMs) developing interpretability or stress-testing algorithms steering model behavior or building agent harnessesnStrong Python and UNIX skills and a demonstrated ability to use agentic coding tools in these environmentsnHistory of applied research in neural network optimization model training or a related areanProven track record of driving scientific investigations and experiments while overcoming obstacles and uncertainty in a research environmentnBS and 5 years of experience MS and 3 years of experience or PhD and 1 year of experience

PhD in a related fieldnPublication record at top AI/ML venuesnExperience with post-training LLMs and network optimization algorithms as well as interpretability or steering techniques for LLMsnExperience of working with large-scale compute infrastructurenExperience shipping a real world product project or featurenExperimental rigor and ablation design when benchmarking LLM optimizationsnStrong communication and accountability skills with a collaborative mindset and strong work ethic