Original job description
About Autonomique
Autonomique is a fast-growing startup building at the forefront of Physical AI: making robots do real work in the real world. We don't just publish papers; we solve the core challenges of autonomy - perception, reasoning, and dexterity - to empower reliable operations for the world’s most demanding industries.
We have achieved significant real-world traction, already deploying bimanual robots in production. We are looking for a core technical leader to join our tight-knit team and help build the future of physical AI.
The Role
We are looking for exceptional, hands-on interns excited about pushing the state of the art in robot manipulation, learning-based control, and real-world deployment. You will work directly with the founding and senior robotics team on problems that sit at the intersection of learning, control, and systems engineering.
This is not a “research-only” or “observer” role - your work will be integrated into our core stack and deployed on physical robots operating under tight latency, safety, and reliability constraints.
What You’ll Do
Deploy Robust Manipulation Algorithms: Design and implement algorithms for complex bimanual tasks (pick/place, insertion, tool use) that don't just work in the lab but perform reliably in dynamic industrial environments.
Develop learning-based robot behaviors using approaches such as Diffusion Policy, imitation learning, and reinforcement learning to improve performance, robustness, and recovery.
Contribute to end-to-end robot autonomy systems that connect perception, semantic task understanding, and low-level robot control.
Build Production-Grade Systems: Contribute production-level C++ and Python code to our core robotics stack, prioritizing testing, maintainability, and real-time performance.
What We’re Looking For
Educational Background: Currently pursuing a MS or PhD in Robotics, Computer Science, or a related field.
Manipulation Experience: Prior work or projects involving robotic arms, dexterous manipulation, inverse kinematics, motion planning, or grasping.
Learning-Based Robotics: Experience with imitation learning, reinforcement learning, or diffusion/sequence models applied to control or robotics.
Vision Skills: Familiarity with computer vision and pose estimation frameworks
Software: Strong proficiency in Python; solid C++ experience is a plus.
ML Frameworks: Deep understanding of PyTorch.
OS: Ubuntu or related Linux.
Bonus Points
Experience with ROS / ROS2 in real systems.
Hands-on work with physical robots (beyond pure simulation).
A strong “builder” mindset - you’re comfortable debugging hardware issues, calibrating sensors, or diagnosing flaky real-world behavior.
First-author publications at top-tier venues (ICRA, CoRL, RSS, CVPR, NeurIPS).
Publicly available code, projects, or research (e.g., GitHub).
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