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Role Description
This role involves developing perception, planning, and control stacks for autonomous systems. You’ll integrate computer vision (CV), SLAM, and reinforcement learning (RL) methods to deliver reliable, real-time performance on constrained hardware.
- Design and implement perception, planning, and control systems for autonomous robots
- Integrate CV, SLAM, and RL to achieve reliable and efficient real-time performance
Qualifications
- Background in robotics, computer science, or engineering
- Proficient in ROS, with hands-on experience in building robotics pipelines
- Strong coding skills in C++ and Python, with libraries like PyTorch, TensorFlow, OpenCV, and NumPy
- Understanding of robotics fundamentals: perception, motion planning, SLAM, and control theory
- Comfortable deploying real-time systems on constrained hardware using CUDA
- Experience with simulation environments such as Gymnasium for RL training
- Care about bridging research and deployment, ensuring robustness in real-world scenarios
Requirements
- Build and optimize robotics software stacks for perception, planning, and control
- Integrate CV, SLAM, and RL algorithms into real-world robotic systems
- Develop scalable robotics pipelines using ROS, PyTorch, TensorFlow, and OpenCV
- Benchmark and validate performance across simulated and real environments
- Optimize models and algorithms for real-time execution on constrained hardware
- Collaborate with researchers and engineers to bring robotics systems from prototype to production
Benefits
- Classified as an hourly contractor to Mercor
- Paid weekly via Stripe Connect, based on hours logged
- Part-time (20–30 hrs/week) with flexible hours—work from anywhere, on your schedule
- Weekly Bonus of $500–$1000 USD per 5 tasks
- Remote and flexible working style
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