Research Scientist, ML H​/W-SW Codesign

# Research Scientist, ML H/W-SW Codesign Sunnyvale, CA Artificial Intelligence+ 6 more Apply now Reality Labs (RL) is bringing Meta's vision to life through virtual reality (VR), augmented reality (AR), and wearable technology. Meeting the demanding compute and power efficiency needs of these products requires purpose-built silicon. The Reality Labs Silicon team is pushing boundaries with pioneering advances in computer vision, machine learning, mixed reality, graphics, displays, sensors, and novel approaches to human body mapping.

Our custom chips will power AR and VR experiences that seamlessly blend the physical and digital worlds throughout everyday life. We're convinced that realizing this vision demands a full-stack approach: spanning transistors, architecture, firmware, and algorithms.

Meta is seeking a HW Research Scientist to join our Research and Development teams. The candidate will have experience working on AI models, hardware acceleration, and software systems related topics. The position will involve taking these skills and applying them to solve for some of the most crucial & exciting problems that exist in Meta Reality Labs. The primary objective will be to develop novel solutions that enable compute and power-efficient training and on-device inference of vision and language models for use cases in AR, VR, and edge devices.

We are hiring in multiple locations.

---## Research Scientist, ML H/W-SW Codesign Responsibilities
* Identify and solve multi-discipline ML acceleration problems involving algorithms, network design, hardware architecture, and AR/VR use cases. Many of these would be first-time solutions in the industry
* Work across hardware and software, to solve deep co-design problems with other Research scientists working in this area
* Codesign and invent novel ML accelerator and system architecture solutions, and facilitate the integration of algorithms and software to utilize these enhancements
* Develop state-of-the-art model compression and scalability techniques using numerics, pruning, distillation, etc