Job description

The Meta Reality Labs Research Team brings together a world-class team of researchers, developers, and engineers to create the future of AR and VR. The Surreal group at RL Research is looking for emerging scientists and researchers with interest in novel computational and communications systems. Our goal is to perform cutting edge research leading to foundational technologies that will underpin Meta’s future contextual AI. We welcome PhD students across computer science, electrical engineering, computer engineering, applied physics, and related disciplines. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year. Create novel systems and components for semantic compression and communication systems that are efficient and achieve high performance through a combination of algorithms and machine learning. Significantly advance the state of the art for end-to-end approaches along the full compression/communications and machine learning pipeline (including sensor captures, wireless transmission, latent representation, neural encoding and decoding). Rapidly design, develop, prototype, execute and analyze the above systems and components for contextual AI systems for future devices. Collaborate with other researchers across various disciplines in a highly cross-functional and interdisciplinary team. Currently has, or is in the process of obtaining a PhD in the field of Computer Science, Electrical Engineering, Computer Vision, Machine Learning, or a related field Demonstrated research experience in Compression, Communications, Computer Vision, Digital Twin, World Model, or Machine Learning 2+ years experience in using Python or C++ for Machine Learning and Computer Vision Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Broad understanding of communications theory and systems - from conventional modulation, coding, beamforming, to more recent digital twin and neural network based approaches Good understanding on end-to-end pipeline for semantic communication systems from source coding, semantic features extraction, channel coding to high level neural network based end-to-end approaches, wireless digital twin, or world mode Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, conferences or journals Demonstrated experience of software, end-to-end system model, or communication system via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub) Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward Experience working and communicating cross functionally in a team environment Intent to return to a degree program after the completion of the internship/co-op

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aimachine learningpython

Platform: meta
Effort: medium

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