Research Scientist, ML H/W-SW Codesign
Meta

Reality Labs (RL) focuses on delivering Meta's vision through Virtual Reality (VR) and Augmented Reality (AR). The compute performance and power efficiency requirements of Virtual and Augmented Reality 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 enable AR & VR devices where our real and virtual world will mix and match throughout the day. 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 Scientist to join our Research & Development teams. The ideal 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 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.
• Optimize models on hardware accelerators to achieve the best performance given various real time latency and power constraints.
• Influence partners to deliver impact through deep, thorough data-driven analysis.
• 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
• Patent and/or publish novel outcomes in peer-reviewed conferences and journals.

Minimum 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.
• 2+ years of specialized experience in one or more of the following machine learning/deep learning domains: Model compression, hardware aware model optimizations, hardware accelerators architecture, GPU architecture, machine learning compilers, or ML systems, AI infrastructure, high performance computing, performance optimizations, or Machine learning frameworks (e.g. PyTorch), numerics and SW/HW co-design.
• Experience developing AI-System infrastructure, AI algorithms or AI hardware acceleration in C/C++ or Python.

Preferred Qualifications:

• 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 or knowledge of on-device algorithm development including hardware-aware ML models and/or optimizing ML compilers for efficient deployment on AI accelerators.
• Experience with PyTorch, TensorFlow or similar machine learning toolsets.
• 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 research and engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
• Experience working and communicating cross functionally in a team environment.
• Experience solving complex problems and comparing alternative solutions, trade offs, and diverse points of view to determine a path forward.

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