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 driving the state of the art forward with breakthrough work 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 is seeking a Research Engineer to join our Research & Development teams. This role involves 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 complex and impactful problems that exist in 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.
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 optimal 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 equivalent experience Experience developing AI-System infrastructure, AI algorithms or AI hardware acceleration in C/C++ or Python Experience with PyTorch, TensorFlow or similar machine learning toolsets Experience working and communicating cross-functionally in a team environment Experience or knowledge of on-device algorithm development including hardware-aware ML models and/or optimizing ML compilers for efficient deployment on AI accelerators Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience or knowledge of training/inference of Large scale AI models - CV and/or LLMs Demonstrated research and engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub) Experience or knowledge of architecting ML hardware accelerators and systems 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 Experience evaluating alternative system or algorithm designs by analyzing trade-offs in performance, power, and latency to recommend a solution Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies