Who We Are:

We build the intelligence that lets robots sense, reason, and act in the real world-moving beyond the lab and into everyday industrial settings like warehouses and factories. Our technology closes the automation gaps that traditional systems can't solve. We are on a mission to redefine how physical work gets done, and we're looking for curious, bold thinkers to help shape the future of robotics with us.

Overview:

We are seeking a highly motivated Research Intern to contribute to our work on task decomposition and multimodal fusion for imitation learning in robotic manipulation. This internship is ideal for students or early-stage researchers passionate about applying machine learning, computer vision, and robotics to real-world industrial automation challenges.

Your Responsibilities:

• Develop and evaluate task decomposition methods for complex robotic manipulation tasks.

• Investigate multimodal sensor fusion (e.g., vision, force, tactile) techniques to improve policy learning.

• Implement and benchmark imitation learning pipelines using both simulated and real-world robotic setups.

• Collaborate with our engineering team to transfer research outcomes to prototype systems.

• Document and present findings to both technical and non-technical stakeholders.

Qualifications:

Must Have:

• Enrolled in a Master's or PhD program in Robotics, Machine Learning, Computer Vision, or a related field with a minimum GPA of 1.7 (German scale).

• OR GitHub repository with at least 5 stars

Other Requirements:

• Familiarity with reinforcement learning, imitation learning, or behavior cloning methods.

• Hands-on experience with robotic platforms or simulation environments (e.g., PyBullet, Isaac Gym).

• Strong programming skills in Python; familiarity with PyTorch or TensorFlow is a plus.

• Excellent problem-solving skills and the ability to work independently.

What We Offer:

• Flexible working hours

• Option to work from home when needed

• A motivated team and an open corporate culture