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