2 days ago
Responsibilities
- Develop and deploy optimization algorithms to accelerate inference for production diffusion models.
- Apply post-training quantization, quantization-aware training, kernel fusion, and model distillation.
- Design hardware-aware architectures that fit compute and memory limits with minimal quality loss.
- Own prototyping, evaluation, and deployment of models into the Apple ecosystem.
- Reason about memory, latency, numerical precision, and hardware-specific constraints.
- Collaborate with hardware engineers, researchers, and software teams on model trade-offs and integration needs.
Requirements
- M.S. or PhD in Computer Science or a related field such as Electrical Engineering, Robotics, Statistics, or Applied Mathematics, or equivalent experience.
- Deep demonstrated expertise in quantization, efficient architecture design, structured pruning, or neural architecture search for GenAI or vision applications.
- Hands-on experience training and fine-tuning diffusion or other large generative models in PyTorch, JAX, or an equivalent framework.
- Ability to reason about efficiency beyond FLOPs, including memory, latency, and numerical precision.
- Preferred: publications at CVPR, ICCV, NeurIPS, ICML, ICLR, or MLSys on efficient generative models.
- Preferred: significant open-source contributions in efficient generative models.
- Strong written and verbal cross-functional collaboration skills.
Tech Stack
Categories
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.