Controls/Robotics Intern

Responsibilities

• Develop and tune controllers (PID, MPC, optimal/robust control) for dynamic, nonlinear systems.
• Build and validate physical models for simulation, hardware-in-the-loop testing, and autonomy.
• Train and deploy ML models for perception, planning, or adaptive control (supervised or RL).
• Integrate algorithms with firmware and cloud teams, ensuring real-time safety and stability.
• Profile, optimize, and verify performance under latency, jitter, and compute constraints
• Comfortable with the pace and intensity of early-stage startup life
• Working towards a bachelor's degree or higher in Computer Science, Robotics or related field
• U.S. permanent residency required
• Preferred Skills and Experience
• Lots of hobby/personal projects and evidence of being a maker
• LiDAR integration and use
• Python/C++ with PyTorch/JAX
• MPC/OSQP/CasADi
• EKF/UKF/factor graphs
• System ID, RL (PPO/SAC) with safety shields
• ROS2, ONNX/TensorRT
• Latency and jitter profiling
• PCB and embedded systems design skills are a huge bonus
• Compensation and Timeline
• $25-35/hr + relocation + company perks
• Fall Internship from August-December depending on your school schedule

Benefits

Additional Information

Company Description Backed by leading climate and American dynamism investors, Terranova builds intelligent robotic systems to terraform the Earth itself - lifting land, restoring wetlands, and protecting critical infrastructure from floods and sea-level rise. Our mission is to preserve the built environment, create new habitats, and usher in an era of abundance. Our work supports climate resilience, disaster recovery, and defense across the United States and beyond. We're assembling a world-class team that wants to work on something real, physical, and civilization-scale. If you want your work to reshape the world (literally), this is the place to do it. What to to Expect We're seeking a mechatronics and controls intern to develop algorithms that give our robotic systems adaptive, intelligent behavior. You'll design and tune controllers, build dynamic models, and integrate them with perception and sensor data. This is a chance to bridge classical control with modern machine learning to shape how machines move through the Earth.

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