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 a small team of engineers who blend ML expertise product intuition and design sensibility. We learn by buildingour 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.

nThis 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 value diverse experiences and unconventional pathswhat 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 applicationsnExperiment with emerging ML techniques and architecturesnBuild end-to-end prototypes that demonstrate ML capabilities in contextnCreate interactive experiences that communicate ideas across Apple and to the broader ML communitynCollaborate with researchers to translate emerging techniques into working systemsnIterate rapidly based on critique treating prototypes as questions rather than answers

Experience training or fine-tuning ML models (PyTorch JAX or MLX)nStrong understanding of ML fundamentals: model architectures training dynamics evaluationnFamiliarity with current ML research landscapenExperience reading and implementing techniques from ML papersnProficiency in Python and Swift; experience with C or Rust a plusnTrack record of building complete working systems rather than isolated components

Experience with LLMs: prompting fine-tuning RLHF inference optimizationnExperience reproducing results from ML papersnFamiliarity with Apple platforms and frameworks (CoreML Metal SwiftUI)nExperience building native apps for iOS or macOSnBackground in an Ru0026D research or prototyping environmentnAbility to work on ambiguous problems where the goal is learning not shippingnBias toward buildingyoud rather make something to test an idea than debate itnInitiative to pursue ideas without waiting for directionnEye for detail and craft in how you present worknComfort communicating ML research to diverse technical audiencesnExperience presenting at or attending ML conferences (NeurIPS ICML etc.)nInterest in AI education or open-source community buildingnWork you can share: models you have trained systems you have built ideas you have explored