AI Robotics/ML Research Engineer - Internship (WFH)

About the internship:We are looking for an AI Robotics & ML Systems Intern who is excited about robotics, AI models, embedded systems, and building real-world intelligent machines.

As an intern, you will work hands-on with robotic platforms, sensors, embedded devices, VLA/robot foundation models, and AI inference systems. You will have the opportunity to experiment with models such as OpenVLA, SmolVLA, GR00T, 0, and similar robotics models and deploy them on real or simulated robotic systems.

Selected intern's day-to-day responsibilities include:

1. Build and experiment with robotics and embedded systems using Arduino, ESP32, STM32, Raspberry Pi, and NVIDIA Jetson.

2. Develop robotics software using Python, C, and C++.

3. Integrate sensors, cameras, motors, actuators, and other hardware components.

4. Work with ROS / ROS 2 and robotics simulation environments.

5. Build and test robot control systems, motion control, and real-time control loops.

6. Work with computer vision, sensor fusion, and AI-based robotics applications.

7. Explore and deploy VLA and robot foundation models such as OpenVLA, SmolVLA, GR00T, 0, and similar models.

8. Experiment with LeRobot and SO-100/SO-101 or similar robotic platforms.

9. Run AI models locally and on GPU-based infrastructure.

10. Debug hardware, software, model, and inference issues.

11. Document experiments, findings, benchmarks, and technical implementations.

12. Work closely with the engineering team to research and evaluate new robotics and AI technologies.Who can apply:Only those candidates can apply who:are available for the work from home job/internshipcan start the work from home job/internship between 26th Aug'26 and 16th Oct'26are available for duration of 6 monthshave relevant skills and interestsare Computer Science Engineering studentsStipend:INR₹ 7,000 - 12,000 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendation, Flexible work hoursSkills required:MATLAB, Python, Robotics, Machine Learning, Embedded Systems, Arduino, C++ Programming, Artificial intelligence and PyTorchOther Requirements:1. Strong interest in robotics, Physical AI, VLA models, and AI-powered autonomous systems.2. Hands-on experience with robotics, embedded systems, Arduino/ESP32/STM32, or Raspberry Pi through academic or personal projects.3. Familiarity with Python/C++, ROS/ROS 2, PyTorch, OpenCV, or Linux-based development.4. Understanding or practical exposure to robot kinematics, sensors, actuators, computer vision, or real-time control systems.5. Candidates who have worked with OpenVLA, SmolVLA, GR00T, π0, LeRobot, SO-100/SO-101, NVIDIA Jetson, or robotics simulation platforms will be preferred.About Company:Hubnine is a data-driven software company leveraging modern AI/ML tools to improve outcomes for our clients.

Responsibilities

We are looking for an AI Robotics & ML Systems Intern who is excited about robotics, AI models, embedded systems, and building real-world intelligent machines.

As an intern, you will work hands-on with robotic platforms, sensors, embedded devices, VLA/robot foundation models, and AI inference systems. You will have the opportunity to experiment with models such as OpenVLA, SmolVLA, GR00T, 0, and similar robotics models and deploy them on real or simulated robotic systems.

Selected intern's day-to-day responsibilities include:

1. Build and experiment with robotics and embedded systems using Arduino, ESP32, STM32, Raspberry Pi, and NVIDIA Jetson.

2. Develop robotics software using Python, C, and C++.

3. Integrate sensors, cameras, motors, actuators, and other hardware components.

4. Work with ROS / ROS 2 and robotics simulation environments.

5. Build and test robot control systems, motion control, and real-time control loops.

6. Work with computer vision, sensor fusion, and AI-based robotics applications.

7. Explore and deploy VLA and robot foundation models such as OpenVLA, SmolVLA, GR00T, 0, and similar models.

8. Experiment with LeRobot and SO-100/SO-101 or similar robotic platforms.

9. Run AI models locally and on GPU-based infrastructure.

10. Debug hardware, software, model, and inference issues.

11. Document experiments, findings, benchmarks, and technical implementations.

12. Work closely with the engineering team to research and evaluate new robotics and AI technologies.