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Meta Reality Labs Research is looking for upcoming scientists and researchers with great interest in algorithm/hardware co-design and acceleration of AI machine learning applications, efficient sensing, and ML algorithms for AR/VR applications. Our team specializes in developing state-of-the-art hardware optimized algorithms for machine perception. With the vision of always-on AI assistance, our team is focusing on cutting edge systems that can facilitate and bring AI assistants powered by LLMs to wearables systems. This internship will focus on aspects of foundational AI technologies around multitasking, multimodal understanding (audio, visual, spatial, etc), privacy, or others that support AI assistant functionalities. Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.
Job Responsibility:
Research on design / model / execution of efficient ML algorithms
Research on novel ML or computational imaging algorithms for applications and optimize existing algorithms
Research on development and optimization of edge computing algorithms (ML and non-ML)
Collaboration with and support of other researchers across various disciplines
Communication of research agenda, progress and results
Prototyping, building and characterizing experimental systems and custom hardware
Requirements:
Currently has, or is in the process of obtaining a PhD in the fields of Computer Science, Electrical Engineering, or related field
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
2+ years research experience in one or more of the following: developing machine learning and computer vision models, optimization of edge computing algorithms, or distributed compute architectures
2+ years experience programming in Python/C++
Experience with Deep Learning frameworks (Pytorch, TensorFlow, etc)
Nice to have:
Experience in multimodal ML algorithms (object detection, classification, tracking, keyword spotting, ASR, etc)
Experience in deploying ML algorithms to real-time with mobile platforms
Experience w/ NAS or developing and deploying algorithms with hardware constraints
Experience w/ pretraining using self-supervised learning
Experience w/ tuning LLM
Experience in privacy preserving ML algorithms
Proven track record of achieving significant results, as demonstrated by first- and/or co-authored publications at leading workshops or conferences (e.g., CVPR, ICCV, TinyML, NeurIPS, ICML), patents, or grants
Intent to return to the degree program after the completion of the internship