Job 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. 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 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

Requirements

Must-haves

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
Track record of building complete, working systems rather than isolated components
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

Nice-to-haves

Proficiency in Python and Swift; experience with C++ or Rust a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythonswiftrust

Platform: apple
Effort: medium