Robotics Engineer, Foundation Model (Open Position)

Telexistence Inc. is a Tokyo-based company specializing in physical AI, focusing on developing foundational models for intelligent robots that operate in real-world environments. Their mission is to make robotics an irreversible reality by creating integrated systems that combine robot foundation models, purpose-built hardware, and operations.

The Robotics Engineer, Foundation Model role involves designing, training, and deploying large-scale multimodal models that integrate vision, language, and action components for real-world robotic applications. Responsibilities include building and maintaining data and training pipelines, collaborating with robotics, controls, and hardware engineers to integrate models into real robot systems, and optimizing models for efficient inference and deployment on embedded/edge hardware.

Candidates should have a background in Machine Learning, Computer Science, Robotics, or a related field, with professional experience in machine learning or deep learning engineering. Hands-on experience training, fine-tuning, or serving large-scale models, strong software engineering skills in Python, and familiarity with large-scale/distributed training and modern ML infrastructure are required. Preferred experience includes robotics (ROS/ROS2), reinforcement learning, deploying models to edge devices, and contributions to publications or open-source projects.

The company offers a collaborative environment where engineers work on foundation models that control real robots in real stores and warehouses, bringing next-generation physical AI from prototype to real-world deployment. The role is based in Tokyo, Japan, and requires professional proficiency in English; Japanese language skills are a plus.

Telexistence Inc. is committed to accelerating the social implementation of physical AI, leveraging large-scale real-world data accumulated in commercial environments such as convenience stores, and a full-stack structure that handles everything from hardware to data collection, foundation models, and control in-house.