Reality Labs (RL) focuses on delivering Meta's vision through Virtual Reality (VR), Augmented Reality (AR) and Wearable AI Devices. The compute performance and power efficiency requirements of our AI devices require custom silicon. Reality Labs Silicon team is advancing research and development in computer vision, machine learning, mixed reality, graphics, displays, sensors, and new ways to map the human body. Our chips will unlock personalized on-device AI capabilities and blend virtual, physical worlds on wearable devices. We believe the only way to achieve our goals is to look at the entire stack, from transistors, through architecture, firmware, and algorithms. Meta's Research & Development teams are looking for a Research Scientist who will invent and productize advanced AI model–hardware codesign techniques. Working at the intersection of AI models, hardware acceleration, and software systems, you will tackle complex, high-impact challenges in Reality Labs, developing novel approaches to compute- and power-efficient training and on-device inference of vision and language models for AR, VR, and edge devices. This role is available in multiple locations.
Responsibilities
Identify and solve multi-discipline ML acceleration problems involving algorithms, network design, hardware architecture, multimodal AI and AR/VR use cases. These may involve novel approaches not yet established in the industry Work across hardware and software, to solve 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 Optimize models on hardware accelerators to achieve target performance given various real time latency and power constraints Influence partners to adopt recommended solutions through data-driven analysis and clear communication of trade-offs Define use cases, and develop methodology & benchmarks to evaluate different approaches Apply in-depth knowledge of how the ML acceleration interacts with the other systems around it Attend conferences, interpret papers, and stay updated with latest research advancements in the field of ML acceleration; contribute to patents and/or publications in peer-reviewed conferences and journals
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD in Electrical Engineering, Computer Science, or relevant technical field, or equivalent practical experience PhD in Electrical Engineering, Computer Science, or equivalent experience Experience developing AI-System infrastructure, AI algorithms or AI hardware acceleration in C/C++ or Python Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience working and communicating cross-functionally in a team environment Demonstrated research and engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub) Experience with PyTorch, TensorFlow or similar machine learning toolsets Experience or knowledge of training/inference of Large scale AI models - CV and/or LLMs Experience or knowledge of architecting ML hardware accelerators and systems Experience evaluating alternative system or algorithm designs by analyzing trade-offs in performance, power, and latency to recommend a solution Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as ICLR, NeurIPS, CVPR, ACL, ICML, MLSys, ISCA, MICRO, DAC, ASPLOS etc Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience or knowledge of on-device algorithm development including hardware-aware ML models and/or optimizing ML compilers for efficient deployment on AI accelerators