RoboForce is an AI Robotics startup building the world’s most advanced Robo-Labor system. Backed by Stanford.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
- Exposure to robot learning using tactile and/or vision-based sensors
- Expertise in object pose estimation, bimanual manipulation, and synthetic data
More about this role
RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. 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.
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...
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