Senior Machine Learning Engineer - Sim2Real & Machine Modeling

About the role

The Autonomy team at Gravis Robotics is seeking a Senior Machine Learning Engineer to bridge the sim2real gap for autonomous construction machinery. You will develop machine and dynamics models, define performance metrics, and work closely with autonomy and simulation teams to ensure controllers function effectively in real-world environments. This role involves designing experiments, analyzing large datasets, and validating model fidelity to improve autonomous performance.

Responsibilities

• Build ML models to bridge the sim2real gap for autonomous controllers.
• Determine optimal architectures for sequence models and state-space formulations based on data.
• Characterize unmodeled effects that impact sim2real transfer.
• Define performance metrics and validation methodologies for model fidelity.
• Develop methods to detect changes in machine properties over time.
• Collaborate with autonomy and simulation teams to influence machine controller development.

Required skills

• Python
• PyTorch
• Git
• Time-series modeling
• Dynamical-system modeling
• Sequence models
• System identification
• State-space approaches
• Machine Learning
• Robotics

Nice to have

• Reinforcement learning
• Imitation learning
• Learning from demonstration
• Control systems
• Hydraulics

Qualifications

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

About the Company

Gravis Robotics is a high-growth Series A start-up backed by SoftBank that brings Physical AI to the construction industry. The company specializes in machine-agnostic retrofit kits that add autonomy to heavy construction equipment, enabling safer and more efficient operations.