Develop and implement motion planning and trajectory optimization algorithms for robotic arm manipulators, build software for scientific instruments, integrate and debug robotics software, develop computer vision capabilities, implement collision avoidance, deploy and maintain robotics software on physical hardware, collaborate with scientists and technical teams, troubleshoot integration issues, improve platform scalability and reliability, contribute to software architecture best practices.
Compensation
Annual base salary: $148,000–$230,000, depending on qualifications and experience. Competitive equity opportunity.
Responsibilities
Develop and implement motion planning and trajectory optimization algorithms for robotic arm manipulators.
Build software that enables robots to operate scientific instruments and laboratory equipment.
Integrate, test, and debug robotics software across simulation environments and physical robots.
Develop computer vision capabilities that allow robots to recognize, manipulate, and interact with laboratory tools and consumables.
Implement collision avoidance and motion control solutions for dynamic laboratory environments.
Requirements
Nice to have: familiarity with computer vision and perception systems
Nice to have: experience with collision detection and avoidance in robotic environments
Nice to have: knowledge of robotics communication and infrastructure tools
Nice to have: experience integrating software with physical robots, sensors, and scientific instruments
Nice to have: exposure to laboratory automation, life sciences, or scientific equipment
Lead research and development of reinforcement learning based autonomous robotics solutions, designing, training and deploying RL controllers for real-world robotic systems.
Locations (2)
Barcelona, Catalonia, Spain
London, England, United Kingdom
Compensation
Competitive salary and VSOP options
Responsibilities
Design and train RL-based controllers for autonomous robots
Integrate vision and sensor fusion for robust control
Develop physics-based simulators and transfer policies to hardware
Ensure safety, efficiency and robustness of deployed policies
Collaborate with cross-functional teams on AI infrastructure
Requirements
MSc or PhD in Robotics, Machine Learning, Control Engineering or related field
Nice to have: PhD with publications in top-tier venues
Nice to have: Experience with dynamic robotic systems
