Do you believe that generative models can fundamentally change how people interact with devices? Are you excited by the possibility of connecting people to unprecedented levels of creativity? Come join the team that turned cutting edge Generative AI research into compelling user experiences through Apple Intelligence. We are part of the Intelligent System Experiences team at Apple and work on the core modeling technology behind Genmoji, Image Playground and Image Wand. From web-scale datasets to distributed training of foundation models, application specific adapters to on-device optimizations, you will have the unique opportunity to advance and ship state-of-the-art technology that impacts Apple users around the world!
We're looking for a Research Engineer to make these models dramatically faster, smaller and cheaper to run on-device and on server.
You'll work across the whole efficiency stack:
Develop and deploy optimizations algorithms to speed up inference of our production diffusion models (PTQ, QAT, kernel fusion, model distillation).
Design hardware-aware architectures that fit within compute and memory limits with minimal loss of quality.
Own the process end-to-end from prototyping, evaluation to deployment into the Apple ecosystem.
Publications at CVPR, ICCV, NeurIPS, ICML, ICLR or MLSys on efficient generative models, or significant open-source contributions in this area.
M.S. or PhD in Computer Science or a related field such as Electrical Engineering, Robotics, Statistics, Applied Mathematics, or equivalent experience.
Deep, demonstrated expertise in at least one of:
Quantization (low-bit and mixed-precision methods, quantization-aware training).
Efficient architecture design, structured pruning or neural architecture search for GenAI or Vision applications.
Strong hands-on experience training and fine-tuning diffusion or other large generative models in PyTorch, JAX, or an equivalent framework.
Comfortable reasoning about efficiency beyond FLOPs, such as memory, latency and numerical precision, and eager to learn hardware-specific constraints.
Excellent cross-functional collaboration skills: You can explain model trade-offs to hardware engineers, hardware constraints to researchers, and integration needs to software teams, in writing and in person.
We’re a diverse collective of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. And the same innovation that goes into our products also applies to our practices — strengthening our commitment to leave the world better than we found it. This is where your work can make a difference in people’s lives. Including your own.
Apple is an equal opportunity employer that is committed to inclusion and diversity. Visit apple.com/careers to learn more.
