Visual Generative Modeling Research Engineer
Santa Clara Valley (Cupertino), California, United States
Machine Learning and AI
Are you excited about Generative AI? Are you excited about how latest advances in this field could transform the way people communicate, create, connect and consume media? Now imagine working to make this vision a reality on beloved mobile platforms used by billions. This is an opportunity to join a dedicated core group in the Intelligent System Experience organization at Apple, a group that will shape the way generative AI technologies transform Apple’s mobile computing platforms.
We are looking for world class technology leaders that have the ability to translate ideas to action, and the hands-on expertise to train and deploy large-scale ML-based features/workflows. In this role your focus will be on visual generative modeling to power applications across computation photography, image and video editing, 3D shape and motion reconstruction, avatar generation, and much more. You will work in a highly cross-functional setting, provide critical technical expertise and leadership, and be responsible for delivering ML solutions that serve the intended experiences while respectiing practical constraints such as memory, latency and power.
We are the Intelligence System Experience (ISE) team within Apple’s software organization. The team works at the intersection between multimodal machine learning and system experiences. System Experience (Springboard, Settings), Keyboards, Pencil & Paper, Shortcuts are some of the experiences that the team oversees. These experiences that our users enjoy are backed by production scale ML workflows. Visual Understanding of People, Text, Handwriting & Scenes, multilingual NLP for writing workflows & knowledge extraction, behavioral modeling for proactive suggestions, and privacy preserving learning are areas our multi disciplinary ML teams focus on.
SELECTED REFERENCES TO OUR TEAM’S WORK:
- https://machinelearning.apple.com/research/stable-diffusion-coreml-apple-silicon (https://machinelearning.apple.com/research/stable-diffusion-coreml-apple-silicon)
- https://machinelearning.apple.com/research/on-device-scene-analysis (https://machinelearning.apple.com/research/on-device-scene-analysis)
- https://machinelearning.apple.com/research/panoptic-segmentation (https://machinelearning.apple.com/research/panoptic-segmentation)
- https://machinelearning.apple.com/research/recognizing-people-photos (https://machinelearning.apple.com/research/recognizing-people-photos)
Key Qualifications
- Strong ML fundamentals
- Hands-on experience with building Deep Learning applications
- Proficiency in using ML toolkits, e.g., PyTorch
- Strong analytical and problem solving skills
- Strong programming skills in Python, C and C++
- You're aware of the challenges associated to the transition of a prototype into a final product
- You're familiar with the challenges of developing algorithms that run efficiently on resource constrained platforms
- You've demonstrated leadership in both applied research and development
- Excellent written and verbal communications skills, be comfortable presenting research to large audiences, and have the ability to work hands-on in multi-functional teams
Description
We are looking for a candidate with a proven track record in applied ML research. Responsibilities in the role will include training large scale generative models in the visual domain on distributed backends, deployment of compact neural architectures such as transformers efficiently on device, and learning adaptive policies that can be personalized to the user in a privacy preserving manner. Ensuring quality in the field, with an emphasis on fairness and model robustness would constitute an important part of the role. You will be interacting very closely with a variety of ML researchers, software engineers, hardware & design teams cross functionally. The primary responsibilities associated with this position range from algorithm design and implementation, ability to integrate research into production frameworks, and collaborating closely with product teams before and after feature launch.
Education & Experience
M.S. or PhD in Electrical Engineering/Computer Science, or a related field (mathematics, physics or computer engineering), with a focus on computer vision and/or machine learning or comparable professional experience; or equivalent experience.