Autonomous Agent Engineering Intern
Robotics and AI Institute
Our Mission Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives. We are looking for Master's and PhD students to work full-time for an extended period (~6 months) in an invigorating environment where researchers and engineers are building the future of robotics and learning-based systems. Strong candidates will bring exceptional talent with solid fundamentals in agents and system design. Your project will focus on developing agentic infrastructure and systems for robot autonomy. If you have a passion for robots and a desire to be part of the advancement of robot capabilities and usefulness, you will want to join us! What You'll Do
- Build and extend an agentic framework for managing on-robot workflows in various deployment scenarios
- Create an internal benchmark for comprehensive evaluation of the agentic workflow in various robotic tasks
- Work with engineers and researchers to integrate your work with powerful simulation tools and on-robot deployment
- Profile, benchmark, and eliminate performance bottlenecks in resource-constrained Linux environments
- Currently pursuing a Master's (MS) or PhD degree in Robotics, Computer Science, Computational Science & Engineering, or a related field
- Prior software engineering experience (e.g., industry internships, significant open-source contributions, or complex project work)
- AI/ML Foundations : Experience with PyTorch and an understanding of modern AI architectures (LLMs, VLMs, or Reinforcement Learning).
- Sim-to-Real Experience: Experience in deploying ML workflows on physical hardware
- Vision-Language Models (VLMs): Experience integrating multimodal models for robotic perception and task planning.
- Simulators: Experience testing code in simulated environments (e.g., Isaac Sim, MuJoCo).
- Robotic Middleware : Familiarity with ROS2
- Robotic Perception : Familiarity with various scene representations (e.g., Gaussian Splattings, Scene Graphs, World Models, SDFs, etc.), perception frontends (e.g. DINO, SAM, SiGLIP etc.), and approaches to scene understanding.
- Robot Learning : Experience in training ML models for robot action policies, robot perception frontends, and state estimation.
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