About the job
As a key pillar of Apple Intelligence, input experience will be the main area where you bring impact to billions of users with your Machine Learning expertise, engineering passion, and programming skills. You will work with a hard-working and dedicated set of outstanding ML and software engineers on a wide range of most advanced text generation technologies such as context-augmented text rewriting, safety-controlled text composition, free-form text transformation, personalized smart interactions, etc. Our team has been working in this area for years and own the NLP and ML text input stack for the keyboard input that includes auto correction, predictive typing on all Apple platforms. We also work on full stack ML applied to NLP and expose these key technologies across Apple on device and also to third party applications through the Natural-Language framework. If you want to amplify your strong Machine Learning and NLP skills into user experiences that will reach every person around you, this is the perfect opportunity!
Responsibilities
No responsibilities listed.
Qualifications
Minimum
Strong machine learning fundamentals
Knowledge of ML techniques such as implementing basic optimizers, applying parameter tuning in model training and evaluation, and reproducing research experiments
Strong programming and communication skills
Ph. D. in CS/EE/Physics/Statistics/etc. (or Masters with 4 years of proven experience)
Preferred
Familiar with model compression algorithms including quantization, pruning, distillations, and experience optimizing large diffusion models or language models
Experience with hardware architecture, software & hardware co-design
Experience with deploying large ML models in real world products
Actively programming with high-quality codes across complex and large repositories
Familiar with common NLP algorithms and applications, including tokenization, language modeling, text decoding, text classifier etc
Experience of multi-modal modeling, presenting plans, progress, and results or demos regularly and concisely