About this Autonomous Agent Engineering Intern role at Rai
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.
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
What You'll need
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).
Areas of Special Interest
While strong core software engineering fundamentals in systems programming are required, no specific domain expertise beyond that is necessary. However, if you happen to possess experience in any single one of the following areas, it would be a massive plus and highly relevant to our current projects:
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.
