Machine Learning Research Scientist - Computer Vision

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.

 

Our Machine Learning (ML) team is looking for Computer Vision Research Scientists with expertise in Multimodal Perception, Spatio-temporal Representation Learning, Few-shot/Zero-shot Learning/Domain Adaptation, Vision-as-Inverse-Graphics (including Differentiable Rendering), or related fields preferred, to improve dynamic scene understanding for robots! We are working on some of the hardest scientific challenges around the safe and effective usage of large robotic fleets, simulation, and prior knowledge (geometry, physics, domain knowledge, behavioral science), not only for automation but also for human augmentation.

 

As a Research Scientist, you will work with a team proposing, conducting, and transferring innovative research. You will use large amounts of sensory data and simulation to address open problems, publish at top academic venues, and test your ideas in the real world (including on our robots of course!). You will also work closely with other teams at TRI to transfer and ship our most successful algorithms and models towards world-scale long-term autonomy and advanced assistance systems.

Responsibilities

• Conduct high-reaching research that solves problems of high value and validates them in benchmarks and systems.
• Push the boundaries of knowledge and the state of the art in ML areas including simulation, perception, prediction, and planning for autonomous driving and robotics.
• Partner with a multidisciplinary team including other research scientists and engineers across the ML team, TRI, Toyota, and our university partners.
• Present results in verbal and written communications, internally, at top international venues, and via open-source contributions to the community.
• Lead collaborations with our external research partners (e.g., Stanford, Berkeley, MIT) and mentor research interns.

Qualifications

• Bachelor’s or Master’s degree in a quantitative field (e.g. Computer Science, Mathematics, Physics, Engineering, Chemistry). Ph.D. in Machine Learning, Robotics, Computer Vision, or related fields preferred.
• Deep expertise in at least one key ML area among Computer Vision, RL, ML theory, AI ethics.
• Consistent record of publishing at high-impact conferences/journals (CVPR, ICLR, NeurIPS, RSS, ICRA, ICCV, ECCV, PAMI, IJCV, etc.) on the aforementioned topics.
• Proficient at scientific python, Unix, and a common DL framework (preferably PyTorch). Experience with distributed learning (especially on AWS) is a plus.
• You can identify, propose, and lead new research projects, working in collaboration with other researchers and engineers to complete it from initial idea to working solution.
• You are intrigued by large-scale challenges in ML, especially in the space of Automated Driving, Robotics, and for societal good in general.
• You are a reliable teammate. You like to think big and go deeper. You care about openness and delivering with integrity.

 

Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.

 

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.