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.
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
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.
Flexible working hours
Option to work from home when needed
A motivated team and an open corporate culture
Skills Required
- Enrolled in a Master's or PhD program in Robotics, Machine Learning, Computer Vision, or related field with a minimum GPA of 1.7 (German scale)
- GitHub repository with at least 5 stars
- 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
Sereact Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Sereact and has not been reviewed or approved by Sereact.
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Retirement Support — The U.S. package includes a 401(k) with a 100% company match up to 6%, signaling strong employer-backed retirement savings. This stands out as generous for a growth-stage startup.
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Healthcare Strength — Comprehensive medical, dental, and vision coverage for employees and dependents is described, indicating broad healthcare support. Such breadth addresses core protection needs across households.
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Leave & Time Off Breadth — 20 days of PTO alongside paid sick leave and paid parental leave are outlined, pointing to a well-rounded time-off offering. These provisions support rest, recovery, and family needs.
Sereact Insights
What We Do
Sereact's mission is to drive your economic growth by closing automation gaps in your intralogistics. Our AI software enables machines to perceive their environment and develop solution strategies on their own, thus qualifying them to become autonomous skilled workers. By leveraging embodied AI for robotics, we ensure that our systems not only think but also physically interact with their surroundings in an intelligent manner. Sereact's AI software for autonomous robotics fully automates pick-and-place processes, making them more efficient, reliable and resilient. Our goal is to optimize your supply chain with minimal integration effort to increase productivity in your warehouse from the first pick while significantly reducing costs.
