Robotics Engineer, Foundation Model

Our mission is to make robotics an irreversible reality. We are always interested in connecting with talented Foundation Model Engineers who are passionate about building the AI that powers real-world robots.

As the Robotics Engineer, Foundation Model, you will design, train, and deploy large-scale multimodal models that integrate vision, language, and action components for real-world robotic applications. Leveraging data from our teleoperation systems, you will create generalizable policies for our robots to perform complex tasks autonomously and reliably—beyond lab-scale or proof-of-concept demos. You will guide the end-to-end pipeline, from data processing and model design to on-robot deployment and performance optimization.

This is an open role intended for engineers at wide experience levels, from mid-career engineers to experienced technical leaders.

Design, train, and fine-tune large-scale foundation models (multimodal, transformer-based, and/or Vision-Language-Action models) for robotic perception, reasoning, and control.

Build and maintain data and training pipelines, including data collection from teleoperation, preprocessing, annotation, and distributed training at scale.

Collaborate with robotics, controls, and hardware engineers to integrate models into real robot systems and evaluate them in production environments.

Design experiments and evaluation protocols to measure model performance, safety, and reliability, and iterate based on real-world deployment feedback.

Optimize models for efficient inference and deployment on embedded/edge hardware.

Track developments in foundation models, LLMs, multimodal and generative AI research, and assess their applicability to TX's products.

Background in Machine Learning, Computer Science, Robotics, or a related field.

Professional experience in machine learning or deep learning engineering, or equivalent research/graduate experience.

Hands-on experience training, fine-tuning, or serving large-scale models, e.g. LLMs, vision-language models, diffusion models, or other multimodal/foundation models.