Robotics Systems Experimentation and Validation Intern en Robust-ai

About you and this role:Robust AI is a fast-growing, early-stage startup founded in 2019 by an unsurpassed team of veterans in robotics, AI and business. We are a collaborative group with a wide range of backgrounds and perspectives, seeking talented individuals who are just as excited about robotics and AI as we are! Help us redefine the very foundation of robotics and build robots that are smart, collaborative, robust, safe, flexible and genuinely autonomous.We're looking for a hands-on intern to support the testing and validation of mobile robotic systems.This role focuses on executing experiments—both in simulation and on real robots—to verifysystem behavior under real-world constraints. You'll contribute to evaluating how navigation,perception, and control systems perform under varying conditions, and help report findings thatshape future development.

Responsibilities:

Assist in debugging issues encountered during testing across the autonomy stack (e.g. navigation, controls, perception).

Collect and process log data to assess system behavior and reliability.

Conduct statistical analyses and summarize results in clear, well-written reports.

Develop and execute experiments—both in simulation and on physical robots—including failure case reproduction, performance benchmarking, and validation scenario authoring.

Execute test plans developed by engineering and product teams for system-level validation.

Required Qualifications:

Proficiency in Python or C++, especially for scripting and data analysis.

Familiarity with Linux-based development and testing environments.

Strong communication skills and attention to detail, especially in technical writing and documentation.

Comfort with robotics experimentation and troubleshooting in resource-constrained and real-time systems.

Nice to Haves:

Experience with ROS1 or ROS2, rviz, and associated developer tools.

Exposure to Docker and containerized workflows.

Familiarity with robotics simulators (e.g., Gazebo, Isaac Sim).

Exposure to cloud or CI tooling (e.g., GitLab runners, AWS, GCP) for experiment execution and test data storage.

Experience working with data aggregation and slicing, or tools like InfluxDB, Pandas, or Jupyter Notebooks for analysis.

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