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 Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.
The Mission
Make general-purpose robots a reality via large-scale embodied AI.
The Challenge
We envision a future where robots assist with household chores and cooking, aid the elderly in maintaining their independence, and enable people to spend more time on the activities they enjoy most. To achieve this, robots need to be able to operate reliably in messy, unstructured environments. Our mission is to answer the question “What will it take to create truly general-purpose robots that can accomplish a wide variety of tasks in settings like human homes with minimal human supervision?”. To answer this, we are gathering large datasets of physical interaction from a variety of sources (including robots and people) and training large generative foundation models on this physical interaction data along with language, video, audio, and other rich modalities.
The Team
Our goal is to revolutionize the field of robotic manipulation, enabling long-horizon dexterous behaviors to be efficiently taught, learned, and improved over time in diverse, real world environments.
Our team has deep cross-functional expertise across simulation, perception, controls, language, vision, multimodal learning, and generative modeling. Success is measured by the advancement of robot capabilities and we’re strong believers in open research. Our north star is fundamental technological advancement in building robots which can flexibly perform a wide variety of tasks in diverse environments with minimal human supervision. Come join us and let’s make general-purpose robots a reality.
The Opportunity
We’re looking for a driven machine learning engineer comfortable working on large integrated machine learning systems. Experience with robots or other embodied systems (such as autonomous vehicles) is a bonus.
If our mission of revolutionizing robotics through machine learning resonates with you, get in touch and let’s talk about how we can create the next generation of AI-powered capable robots together.
Responsibilities
- Collaborate with internal research scientists and our partner labs at top academic research universities, including MIT, Stanford, Berkeley, CMU, Columbia, and Princeton to drive pioneering research at scale.
- Build, improve, and robustify end-to-end integrated ML pipelines for training multimodal (language, images, video, actions) models at scale.
- Train, finetune, and serve robot foundation models with a strong MLOps mindset.
- Build processes for integrating collaboration-produced and open-source advancements and code into our internal stack.
- Build and improve large data pipelines for foundation model training.
- Be a key member of the team and play a critical role in rapid progress measured by both the development of internal capabilities and high-impact external publications.
Qualifications
- 2+ years of professional ML engineering experience at an AI/ML-focused organization.
- Familiarity with the state-of-the-art in behavior learning, language, and/or computer vision.
- Experience training large-scale foundation models (VLMs, text-to-video models, etc) utilizing distributed training and high-performance optimization techniques such as quantization, mixed precision, model parallelism, data parallelism or FSDP.
- Extensive practical experience with PyTorch.
- Strong proficiency in Python and software development best practices such as unit testing, documentation, code review, continuous integration, and dependency management.
- Familiarity with data pipelines, model serving and optimization, cloud training, and dataset management.
- An ability to move fast and switch between modes of rapid prototyping and robust implementation as required.
Nice to haves
- Experience deploying models on embodied systems/robots.
- Experience working in mixed teams of research scientists and engineers.
- Experience Amazon EC2, S3, and/or Sagemaker.
- Experience with Bazel.
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