Machine Learning Engineer Intern, AI & Robotics
Required skills
- Machine Learning Engineer Intern
- AI & Robotics
- Physical AI
- Robo-Labor
- industrial environments
- advanced AI capabilities
- robot foundation models
- world-action models
- precise, contact-rich manipulation
- tactile sensing
Nice to have
- robot learning
- tactile-based sensors
- vision-based sensors
Role summary
RoboForce, an AI robotics company, seeks a Machine Learning Engineer Intern to develop Physical AI-powered robots for industrial environments. The intern will contribute to building the AI ecosystem, focusing on data collection, labeling, training, evaluation, and deployment. Responsibilities include training robot foundation models (VLA, world-action), exploring new methods like reinforcement learning and tactile sensing, building data pipelines, running evaluations, and deploying models on robots. Collaboration with AI engineers, robotics, hardware, and product teams is essential. This is a 3-6 month, full-time, onsite internship starting November 2026.
Why RoboForce
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.
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.
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