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

tomorrow's 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. We're 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.

DESCRIPTION

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. You'll iterate rapidly and communicate what you've

learned. We value diverse experiences and unconventional paths—what matters is

that you can build, learn, and communicate what you've discovered. We're looking

for someone who brings something we don't already have, whether that's a novel

technical specialty, an unusual background, or a way of thinking we haven't

encountered. If you build things to understand them, we want to hear from you.

MINIMUM QUALIFICATIONS

Experience 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 components

PREFERRED QUALIFICATIONS

Experience 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—you'd 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