Robots to Automate Fulfillment. Backed by Y Combinator.
About the role
Improve our data annotation pipeline. Clean up existing workflows, improve data validation and labeling tools, and make it easier to turn robot demonstrations and deployment data into useful training datasets. Maintain and improve teleoperation software. Improve how operator inputs translate into robot behavior, including action smoothing, motion mapping, responsiveness, and debugging tools. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong software engineering skills in Python and C++, with experience writing and debugging code that interacts with physical hardware
- A strong understanding of robotic kinematics, coordinate frames, inverse kinematics, and motion control
- Hands-on experience with robot arms, teleoperation, or related robotic systems
- Experience writing and debugging CAN communication with actuators or other hardware
- The ability to design useful experiments, define success criteria, and diagnose problems across software and hardware
- Good engineering habits: readable code, practical tests, clear documentation, and comfort integrating work across Git branches
More about this role
Yondu builds software that turns off-the-shelf robots into autonomous warehouse labor. We’re looking for a hands-on engineer to build and improve the tools that connect our hardware, AI models, and real-world operations.
Your work will span robot control, teleoperation, data annotation, and prototype hardware testing. You’ll investigate new ideas, measure what works, and turn promising experiments into tested, reusable software that other engineers can build on.
As we introduce new hardware, warehouse tasks, and models, you’ll own the development and continued improvement of our R&D tools and experimental systems. You’ll work closely with our AI and production engineers, who will carry validated capabilities through production integration and deployment.
Improve our data annotation pipeline. Clean up existing workflows, improve data validation and labeling tools, and make it easier to turn robot demonstrations and deployment data into useful training datasets.
Maintain and improve teleoperation software. Improve how operator inputs translate into robot behavior, including action smoothing, motion mapping, responsiveness, and debugging tools.
Develop and evaluate manipulation...
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