Internship - Robot Control Systems (Fall 2026)

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.

Learn more at https://fieldai.com.

At FieldAI, we build autonomous robotic systems that operate in demanding, real-world environments where tight integration between hardware and software is critical. We’re looking for a Robotics Controls Intern to join our autonomy team and work directly alongside our senior engineers to tackle complex control challenges for large-scale, off-road vehicles. In this highly impactful internship, you will help bridge the gap between advanced control theory and field-deployable products. You will dive into system dynamics modeling, optimize GPU-accelerated control libraries, and develop computationally constrained control schemes for heterogeneous platforms, ranging from massive off-road vehicles to smaller, resource-limited robotic systems. Furthermore, you will play a critical role in designing and implementing low-latency safety layers that protect our robots in both tele-operated and fully autonomous modes. This is a hands-on role for a driven researcher or engineer who wants to see their code running on real vehicles in extreme, unstructured environments.

• Currently pursuing a Ph.D. or Master’s degree in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or a highly related field with a focus on control systems.
  
  

• Deep theoretical understanding and practical experience with advanced control methodologies, particularly predictive and sampling-based control (e.g., MPPI, MPC).
  
  

• Strong proficiency in GPU programming (specifically CUDA) with a track record of accelerating and optimizing complex algorithms for real-time execution.

• Hands-on experience taking control algorithms out of simulation and deploying them onto physical robots in real-world environments.
  
  

• Experience with system dynamics modeling, system identification, and training learning models for robotic platforms.
  
  

• Strong software engineering skills in C++ and Python, with the ability to write clean, deployable code for robotics applications.
  
  

• Hands-on experience with Linux, ROS1/2 and  Docker

• Experience working with large-scale, off-road, or high-speed autonomous wheeled vehicles in unstructured environments.
  
  

• Experience designing and implementing low-latency safety layers or safety controllers for autonomous or tele-operated systems.
  
  

• Demonstrated ability to reduce compute costs and adapt computationally heavy control schemes for hardware-constrained systems.
  
  

• Experience maintaining or significantly contributing to open-source robotics control libraries.
  
  

• Familiarity working within established, fast-paced autonomy engineering teams and seamlessly integrating with existing software stacks.
  
  

• Knowledge of containerization (Kubernetes) and modern DevOps practices.