Research Scientist Intern, Efficient World Foundation Models (PhD) Responsibilities
Research on design / model / execution of efficient ML algorithms
Develop and optimize novel ML, computational imaging, and generative AI algorithms for edge applications
Research on development and optimization of edge and distributed computing algorithms
Collaboration with and support of other researchers across various disciplines
Communication of research agenda, progress and results
Prototyping, building and characterizing experimental systems with real hardware
Minimum Qualifications
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)
Preferred Qualifications
Experience with customizing Multi-Modal LLM
Experience with world foundation models, self-supervised learning, and embodied AI
Experience with generative models and high-quality visual generation
Experience in deploying ML algorithms for real-time execution on mobile and distributed platforms
Experience with NAS or developing and deploying algorithms with hardware constraints
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
