Sim2Real ML Engineer for Autonomous Construction Robots

Gravis-Robotics; Zürich; Vor Ort; CHF 120.000 - 180.000; Vollzeit

Zusammenfassung

Gravis Robotics is seeking a highly skilled ML/RL-focused engineer to bridge the sim2real gap for autonomous earthmoving machines using ML/DL methods. You will build models, decide architectures backed by data, and identify unmodeled effects that affect transfer to real robots.

You will work closely with autonomy and simulation teams, own data requirements, and define metrics to validate fidelity and transfer performance across a range of heavy equipment.

Qualifikationen

• Degree in Computer Science, Robotics, Machine Learning, Engineering, or a related field.
• Strong Python and PyTorch, strong git skills.
• Solid experience modeling time-series or dynamical-system data from large datasets; sequence models, system identification, or state-space approaches.
• Strong analytical skills: you design the experiment, run the ablation, and draw a conclusion you'd defend.

Aufgaben

• Build ML models to help bridge the sim2real gap.
• Decide what architecture the problem actually needs - sequence models, state-space formulations, something else - and back the choice with data.
• Characterize where the gap actually comes from: which unmodeled effects hamper the sim2real transfer, and which we can safely ignore.
• Answer how much data is needed and what distribution it has to cover.
• Define the performance metrics and validation methodology for model fidelity and sim2real transfer.
• Build models and methods that detect machine properties changing over time.
• Work closely with the autonomy and simulation teams - your models influence the controllers that run on the machine.

Kenntnisse

Python

PyTorch

Git

Time-series modeling

Dynamical systems

Analytical skills

Ausbildung

Degree in Computer Science, Robotics, Machine Learning, Engineering, or related field

Jobbeschreibung

Gravis Robotics is seeking a highly skilled ML/RL-focused engineer to bridge the sim2real gap for autonomous earthmoving machines using ML/DL methods. You will build models, decide architectures backed by data, and identify unmodeled effects that affect transfer to real robots.

You will work closely with autonomy and simulation teams, own data requirements, and define metrics to validate fidelity and transfer performance across a range of heavy equipment.