AIML - ML Prototyping Engineer, Machine Learning Research

The Machine Learning Research Prototyping team sits at the intersection of cutting-edge research and real-world impact. We serve as the bridge between groundbreaking AI and ML discoveries and the innovative products that define tomorrows user experiences. Our nimble, collaborative team transforms theoretical breakthroughs into tangible prototypes that help Apple understand where AI could go next, and help the broader ML community build on Apple research.

Were a small team of engineers who blend ML expertise, product intuition, and design sensibility. We learn by building-our prototypes range from novel architectures to complete interactive experiences, whatever best illuminates a research idea. We work at the boundary between what models can do today and what Apple products and the ML community will need tomorrow.

This role spans the full stack of AI development: from training and fine-tuning models, to building the systems that serve them, to crafting the interfaces that let people experience them. Youll iterate rapidly and communicate what youve learned.

We value diverse experiences and unconventional paths-what matters is that you can build, learn, and communicate what youve discovered. Were looking for someone who brings something we dont already have, whether thats a novel technical specialty, an unusual background, or a way of thinking we havent encountered. If you build things to understand them, we want to hear from you.Train, fine-tune, and adapt foundation models for novel applications Experiment with emerging ML techniques and architectures Build end-to-end prototypes that demonstrate ML capabilities in context Create interactive experiences that communicate ideas across Apple and to the broader ML community Collaborate with researchers to translate emerging techniques into working systems Iterate rapidly based on critique, treating prototypes as questions rather than answersExperience training or fine-tuning ML models (PyTorch, JAX, or MLX) Strong understanding of ML fundamentals: model architectures, training dynamics, evaluation Familiarity with current ML research landscape Experience reading and implementing techniques from ML papers Proficiency in Python and Swift; experience with C++ or Rust a plus Track record of building complete, working systems rather than isolated componentsExperience with LLMs: prompting, fine-tuning, RLHF, inference optimization Experience reproducing results from ML papers Familiarity with Apple platforms and frameworks (CoreML, Metal, SwiftUI) Experience building native apps for iOS or macOS Background in an R&D, research, or prototyping environment Ability to work on ambiguous problems where the goal is learning, not shipping Bias toward building-youd rather make something to test an idea than debate it Initiative to pursue ideas without waiting for direction Eye for detail and craft in how you present work Comfort communicating ML research to diverse technical audiences Experience presenting at or attending ML conferences (NeurIPS, ICML, etc.) Interest in AI education or open-source community building Work you can share: models you have trained, systems you have built, ideas you have explored