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Machine Learning Research Intern, Embodied Foundation Models and Robot Learning
Toyota Research Institute
Los Altos, CA, USA
Posted on Friday, November 3, 2023
At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team in Human-Centered AI, Human Interactive Driving, Energy and Materials, Machine Learning, and Robotics.
This is a Summer 2024 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.
Our Machine Learning (ML) team is looking for Research Interns for Summer 2024 in a variety of areas such as Multi-Modal Foundation Models, Robotics, Reinforcement Learning, Planning & Control, Uncertainty Estimation, and Safety-Aware & Robust ML. We are aiming to make progress on some of the hardest scientific challenges around the safe and effective usage and development of machine learning algorithms within robotics, not only for automation, but for human augmentation. To this end, the mission of the ML team is to develop robust algorithms for robots, working towards Principle-Centric Artificial Intelligence (AI) for Embodied Foundation Models.
As a Research Intern, you will work with a multidisciplinary team proposing, conducting, and transferring pioneering research in Machine Learning. You will use large amounts of sensory data and simulation to solve open problems, work towards publications at top academic venues, and test your ideas in the real world, including on our robots, of course!
- Conduct daring research primarily in Robotics that solves open problems of high practical and/or ethical value and validate it in real-world benchmarks and systems.
- Push the boundaries of knowledge and the state of the art in robotics and embodied foundation models.
- Partner with a multidisciplinary team, including other research scientists and engineers across the Machine Learning team, TRI, Toyota, and our university partners.
- Stay up to date on the state-of-the-art in Machine Learning ideas and software.
- Present results in verbal and written communications at international conferences, internally, and via open-source contributions to the community.
- Currently pursuing a Ph.D. in Machine Learning, Robotics, or related fields.
- Publication or desire to publish at high-impact conferences/journals (e.g., CoRL, ICLR, NeurIPS, ICML, UAI, AISTATS, TMLR, RSS, ICRA, IROS, RA-L, T-RO, CDC, L4DC, etc.) on some of the aforementioned topics.
- Passionate about large scale challenges in ML grounded in physical systems, especially in the space of robotics.
- Proficiency with one or more coding languages and systems, preferably Python, Unix, and a Deep Learning framework (e.g., PyTorch).
- Ability to work in collaboration with other researchers and engineers of the Machine Learning team to invent and develop interesting research ideas.
- A reliable teammate who loves to think big, go deeper, and strives to deliver with integrity.
Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.
The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package including vacation and sick time. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
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TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.