Autonomy ML Intern: Build & Benchmark Construction Robots

Gravis Robotics; Zürich; Vor Ort; CHF 17.000 - 23.000; Teilzeit

Zusammenfassung

Gravis Robotics seeks driven ML interns to contribute to the design, testing, and deployment of machine learning models for autonomous heavy machinery. You will work within the Autonomy team, designing experiments, benchmarking models, and helping define metrics and data requirements for robust performance.

The role emphasizes autonomous problem solving, collaboration, and hands-on development with PyTorch, Python, and robotics tools in a dynamic startup environment.

Qualifikationen

• Proficiency with Python, and Git.
• Familiarity with popular deep learning libraries (PyTorch, etc.).
• Experience with data analysis, ML optimization, and hyperparameter tuning.
• Strong analytical and problem-solving skills, with the ability to interpret experimental results and draw sound conclusions.

Aufgaben

• Design, test, and deploy novel ML models for autonomous heavy machinery
• Benchmark and analyze model performance, including conducting ablation studies to evaluate key design choices.
• Help define performance metrics, validation methodologies, and explore anomaly detection methods to understand the limitations of each architecture and which kind of data is needed for robust performance of the ML models.
• Collaborate closely with the rest of the teams to improve the reliability and scalability of the ML pipeline.

Kenntnisse

Python

Git

Data analysis

Analytical thinking

Problem solving

Tools

PyTorch

NVIDIA Isaac Sim

ROS/ROS 2

C++

Jobbeschreibung

Gravis Robotics seeks driven ML interns to contribute to the design, testing, and deployment of machine learning models for autonomous heavy machinery. You will work within the Autonomy team, designing experiments, benchmarking models, and helping define metrics and data requirements for robust performance.

The role emphasizes autonomous problem solving, collaboration, and hands-on development with PyTorch, Python, and robotics tools in a dynamic startup environment.