Build, improve, and robustify end-to-end integrated ML pipelines for training multimodal (language, images, 3D, video, actions) models at scale.
Develop and manage physics-based robot simulation environments enabling scalable training and evaluation of learning-based behavior models in realistic, physically grounded scenarios.
Integrate and validate learned policies in simulation, assessing real-world applicability, generalization, and performance across diverse environments, tasks, geometries, and sensor viewpoints.
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.
Collaborate with internal research scientists and our partner labs at top academic institutions and Toyota research labs to drive pioneering research at scale.
Requirements
Bachelor's or master's degree in Computer Science, Robotics, Physics, or a related field.
2+ years of professional engineering experience at an AI/ML-focused organization.
Strong proficiency in Python and experience with simulation frameworks such as Isaac Sim, PyBullet, MuJoCo, or similar.
Hands-on experience with robotics simulation, reinforcement learning, or large-scale machine learning.
Familiarity with state-of-the-art methods in behavior learning and/or computer vision.
Experience integrating ML models into simulated or real-world environments.
Extensive practical experience with PyTorch.
Ability to alternate between rapid prototyping and production-quality implementation.
Solid understanding of software engineering best practices, including testing, CI/CD, and documentation.
Benefits
medical, dental, and vision insurance
401(k) eligibility
paid time off benefits (including vacation, sick time, and parental leave)
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