THRYVE is partnering with a world-leading robotics company pushing the boundaries of robot learning, dexterous manipulation, and real-world autonomy.
We're looking for a Senior Robotics Engineer who thrives at the intersection of robotics, machine learning, and human-robot interaction. You'll help develop next-generation robotic systems that learn from human demonstrations and operate reliably in complex, unstructured environments.
This is an opportunity to work on cutting-edge manipulation challenges, leveraging teleoperation, imitation learning, and state-of-the-art AI to create robots capable of performing increasingly sophisticated real-world tasks.
What You'll Do
- Architect and develop robot learning pipelines for large-scale, high-quality data collection and training.
- Design and integrate advanced teleoperation systems, including force-feedback, haptics, and multimodal sensory interfaces.
- Advance robotic manipulation capabilities, with a particular focus on dexterous robotic hands and learning from human demonstrations.
- Collaborate closely with world-class AI researchers, roboticists, and software engineers to move cutting-edge research into deployed systems.
- Contribute to the full lifecycle of robotic learning systems, from data acquisition and model development through to real-world deployment and validation.
What We're Looking For
- Strong expertise in robot learning, teleoperation, imitation learning, and human-in-the-loop robotics.
- Understanding of Vision-Language Models (VLMs), Vision-Language-Action (VLA) architectures, and their application to embodied AI and robotic manipulation.
- Deep understanding of manipulation, control systems, and real-world robotic deployment challenges.
- Advanced programming skills in C++ and Python.
- Hands-on experience with ROS / ROS2 and modern robotics software stacks.
- Experience developing and deploying robotic systems beyond simulation.
Highly Desirable
- Diffusion-based policies and generative models for robotics.
- Learning from Demonstration (LfD) or Reinforcement Learning.
- VR/AR-based teleoperation interfaces.
- Unity, Isaac Sim, MuJoCo, or similar simulation environments.
- Experience working with dexterous hands, tactile sensing, or embodied AI systems.