Machine Learning Engineer Intern, AI & Robotics

The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are seeking a Machine Learning Engineer Intern to join us in developing advanced AI capabilities for robotics.

The internship starts from November 2026 for 3-6 months. As part of our team, you will help build the foundational AI ecosystem, spanning data collection, labeling, training, evaluation, and deployment.

You will collaborate closely with a team of world-class AI engineers and gain hands-on experience solving real-world challenges at the intersection of artificial intelligence and robotics.

Responsibilities • Train and improve robot foundation models, including vision-language-action (VLA) models and world-action models, for precise, contact-rich manipulation in industrial settings.

• Explore new modalities and methods, including tactile and force-torque sensing, reinforcement learning, and simulation, to advance model capabilities.

• Build data pipelines for robot learning, including data collection, curation, labeling, and dataset quality assurance. • Design and run evaluations in simulation and on real robots to measure and improve model performance.

• Deploy models on robots and optimize them for real-time onboard inference. • Work closely with robotics, hardware, and product teams to bring models from research to production.

Requirements • Currently pursuing an advanced degree in Computer Science, Robotics, Machine Learning, or a related field, with a graduation date in 2026 or 2027.

• Proficiency in multimodal models, modern machine learning architectures (e.g., transformers and diffusion models), and reinforcement learning. • Proficiency in deep learning frameworks such as PyTorch, JAX, or TensorFlow.

• Requires 5 days/week in-office collaboration with the teams. Bonus Qualifications • Exposure to robot learning using tactile and/or vision-based sensors.

• Expertise in object pose estimation, bimanual manipulation, and synthetic data.