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Our team is seeking a talented Applied Scientist Intern to join us for 3-6 months and propel our ambitious research in embodied foundation models forward. We’re a team of Applied Scientists, Machine Learning Engineers, and Software Engineers who strive to expand the horizons of embodied AI beyond simply reacting to perceptual inputs toward reasoning over them to handle even the most complex and rare situations. Our projects encompass some of the hardest problems in AI and require leveraging the latest research, state-of-the-art models, rigorous engineering, and cross-functional collaboration.
Job Responsibility:
Work on foundation models for embodied AI, including large-scale pretraining, post-training, leveraging language, or improving reasoning capabilities
Train models on large-scale multimodal (vision, language, etc.) data efficiently in a multi-node distributed system, and evaluate their performance on open (and closed) datasets/benchmarks
Lead a high-impact research work and publish at a top tier conference
Requirements:
Currently pursuing a graduate degree in Computer Science, Machine Learning, Robotics, or related technical field
Proficient in at least one backend/systems programming language (e.g. Python, Ruby, Java, etc)
Previous experience in vision-language models, large language models, natural language processing, especially around reasoning
Solid software engineering fundamentals, especially in Python
Previously used PyTorch or a similar library for deep learning (e.g. Tensorflow, JAX)
Experience with multi-node distributed training of large models
Interested in using large-scale multimodal (vision, language, etc.) datasets to improve embodied AI
Previous publications in conferences (e.g., CVPR, ICCV, CoRL, NeurIPS, CoLM, RSS, ICRA, among others)