SWE – Applied Machine Learning Engineer, Developer Publications Full-time

Summary
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something – you’ll add something.

Our developer documentation department seeks a creative engineer who has a robust knowledge of large language models (LLMs), generative AI, and experience with code generation. Our ideal candidate has the ability to fine-tune pre-trained models, and work collaboratively in a multi-discipline team. Strong engineering fundamentals and a passion for code generation models are required.

Description
As an Applied ML Engineer in the Developer Publications Intelligence team, you will join a multi-discipline team of passionate engineers to design, fine-tune, and produce models that will be used by existing and future tools produced by Apple for third-party developers. We are looking for exceptional candidates to help define the future of Swift code generation in our tools and beyond. Your role is to ensure Apple and our third-party developers using Xcode deliver extraordinary software products to millions of customers around the world!

Your duties will include:
– Fine-tuning models and making them available for engineers to evaluate and define further experiments
– Actively engaging in all aspects of model development, from ideation, training, experimentation to deployment
– Collaborating with data collection, model evaluation, and tool integration teams to develop and implement model solutions
– Developing and maintaining frameworks and tools to help facilitate the model fine-tuning process