Software Engineer, AI Co-design

About the Role

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 Software 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 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.

Key Responsibilities

• Identify and solve multi-discipline ML acceleration problems involving algorithms, network design, hardware architecture, and AR/VR use cases.
• Work across hardware and software to solve co-design problems with other Research scientists.
• 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.
• Optimize models on hardware accelerators to achieve the best 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 and benchmarks to evaluate different approaches.
• Apply in-depth knowledge of how ML acceleration interacts with other systems.
• Attend conferences, interpret papers, and stay updated with latest research advancements in ML acceleration.
• Contribute to patents and/or 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 architecting ML hardware accelerators and systems.
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).

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.
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact.
• 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.
• 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 training/inference of Large scale AI models - CV and/or LLMs.
• 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.