AI Research Scientist, SysML

About the Role

Reality Labs (RL) focuses on delivering Meta's vision through Virtual Reality (VR), Augmented Reality (AR) and wearable devices. 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 wearable devices, where our real and virtual world will mix and merge 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 and 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 most crucial and 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.

Key Responsibilities

• Identify and solve multi-disciplinary ML acceleration problems spanning algorithms, model architecture, software, and hardware for AR, VR, and wearables use cases, often requiring novel approaches not yet established in the industry
• Invent and develop state-of-the-art model compression, efficiency, and scalability techniques, including numerics, pruning, distillation, speculative decoding, and memory-efficient transformer architectures, and benchmark them across diverse network classes. Integrate proven research into production ML infrastructure for broad adoption across the organization
• Post-train and optimize foundation models to deliver Pareto-optimal multimodal LLMs and GenAI models on our target hardware under real-time latency, power, and memory constraints
• Use models and algorithm insights to co-design next-generation hardware and system architectures, partnering with research scientists and silicon teams, and drive the algorithms and software integration needed to exploit these enhancements, applying in-depth knowledge of how ML acceleration interacts with surrounding systems
• Define use cases, methodology, and benchmarks to evaluate competing approaches, and influence partners to adopt recommended solutions through data-driven analysis and clear communication of trade-offs
• Stay at the forefront of efficient on-device ML research, and contribute to patents and publications in peer-reviewed conferences and journals

Requirements & Tech Stack

• 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 evaluating alternative system or algorithm designs by analyzing trade-offs in performance, power, and latency to recommend a solution
• Experience or knowledge of on-device algorithm development including hardware-aware ML models and/or optimizing ML compilers for efficient deployment on AI accelerators

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 working and communicating cross-functionally in a team environment
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience or knowledge of architecting ML hardware accelerators and systems
• Experience or knowledge of post-training inference of Large scale GenAI/LLM models
• 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 adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated research and engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)