research scientist

Meta Reality Labs Research (RLR) is dedicated to the research and development required to bring virtual and augmented reality to billions of people around the world. At our lab, we aspire to a vision of social VR and AR, where people are able to interact with each other across distances in a way that is indistinguishable from in-person interactions. Meta RLR is looking for a talented Research Engineer to accelerate the progression to authentic social presence in Virtual Reality and Augmented Reality. In this role, you will be a member of the Mobile Telepresence team which has been committed to comprehensive mobile optimization that delivers end2end telepresence with high energy efficiency. The mobile optimization including but not limited to neural network architecture innovation, model compression, and low bits inference for dedicated computing hardwares directly guides performance and deployment of telepresence solutions on future Meta ARVR products and push forward the State-of-the-art on mobile efficiency. You will be working on designing novel model architecture with high hardware efficiency, standardizing optimization algorithms into mobile compiler and runtime engine, and creating tools/flow to accelerate telepresence deployment on the current & next generation of ARVR hardware platforms. You will work closely with AI and telepresence researchers to analyze telepresence algorithms (which are mixed operators of computer vision, deep learning models, and graphic rendering processing) and apply codesign methodologies from the early phase. You will also partner with software engineering teams to validate optimization solutions on ARVR hardware which has off-the-shelf SoCs and/or inhouse neural network accelerators. You will apply software development best practices to design features, optimization, and performance tuning techniques. You will gain valuable experience in developing product driven computing solutions and will help in driving next generation hardware software codesign for AI domain specific problems. Research Scientist - Mobile Optimization Responsibilities <ul><li>Research and development of novel optimization algorithms/techniques for achieving high energy efficiency on mobile computing platforms. </li><li>Development of optimization flow for telepresence models with native toolchain integration to accelerate compilation and deployment on the next generation of ARVR platforms. </li><li>Collaborate with other research teams to analyze telepresence algorithms and co-design optimized algorithms/models to balance performance between distortion and latency. </li><li>Collaborate with the productization team to guide product architecture design and accelerate the next generation of telepresence solutions with both off-the-shelf SoCs and inhouse accelerators. </li></ul>Minimum Qualifications <ul><li>Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta. </li><li>Currently has, or is in the process of obtaining a PhD degree in computer vision, machine learning, computer graphics, artificial intelligence, applied mathematics, or related areas. </li><li>1+ years practical industry or research experience. </li><li>First-author publications at peer-reviewed AI conferences (e.g. DAC, ISCA, HPCA, CVPR, ECCV, NeurIPS, ICML, and ICLR). </li><li>Proven C/C++ and Python programming skills. </li><li>Knowledge of GPU, CPU, DSP, or AI hardware accelerator architectures with good understanding of system performance evaluations from such as end2end latency, memory bandwidth, I/O access, compute utilization analysis with necessary tooling development. </li><li>Experience on optimizing image processing and computer vision applications for edge devices with optimizations such as vectorization, parallelization, and hardware specific SIMD and deep learning algorithms with techniques such as neural networks architecture search, pruning, and low bits quantization. </li><li>Experience with optimization on Variational Auto-Encoder, Transformers, Graph CNN, and stable diffusion models. </li><li>Familiar with frameworks like OpenCV, PyTorch, TensorFlow Lite, and ONNX. </li></ul>Preferred Qualifications <ul><li>Familiar with embedded systems, Linux OS and driver development, and basic knowledge on Android application development. </li><li>Experience with custom compiler design that's similar to MLIR and TVM. </li><li>Experience on graphics programming and 3D rendering with OpenGL/Vulkan is a plus. </li><li>Experience with algorithms optimization for Qualcomm SoCs is a plus. </li></ul> Start preparing Learn about how to prepare for your interview with our interview guide, tips, and interactive experiences. Visit interview prep

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