Senior RL Engineer – Real-World Robotics Autonomy

Gravis Robotics; Austin (TX); On-site; USD 110,000 - 180,000; Full time

Job summary

Gravis Robotics in Austin designs autonomous control for heavy machinery. You will develop data-driven planning and control modules that run across diverse excavator models and sites, with attention to sim2real transfer and production deployment.

The role blends hardware and software, requiring practical experience with real robots and robust software practices. As part of the autonomy team, you will implement learning-based planning solutions, collaborate with engineers, and mentor junior

Qualifications

• 2–5 years of industry experience developing RL systems for control and/or planning and deploying them on real robots with a customer
• Experience with GPU-accelerated simulation environments (e.g. IsaacSim/IsaacLab, CARLA, MuJoCo)
• Strong Python skills and experience with PyTorch or similar libraries
• Proficiency in C++ and debugging real-world system behavior
• Ability and willingness to travel as required by business projects.

Responsibilities

• Develop data driven planning and control systems for autonomous excavation that generalize across machine models and soil conditions
• Contribute to simulation improvements that reduce or address the sim2real gap
• Define data collection and curation pipelines for incorporating real data in policy training
• Design experiments focused on continuous performance and robustness improvements
• Explore the usage of adaptive and online reinforcement learning in deployed systems
• Provide mentorship and supervision for junior team members, interns, and students
• Integrate learned components into a larger software stack
• Collaborate with excavation and motion planning engineers
• Build tools for analysing and evaluating the behavior of learned components

Skills

RL for control/planning

GPU-accelerated sims

Python (PyTorch)

C++ proficiency

Debugging real-world systems

Travel willingness

Tools

IsaacSim/IsaacLab

CARLA

MuJoCo

Job description

Gravis Robotics in Austin designs autonomous control for heavy machinery. You will develop data-driven planning and control modules that run across diverse excavator models and sites, with attention to sim2real transfer and production deployment.

The role blends hardware and software, requiring practical experience with real robots and robust software practices. As part of the autonomy team, you will implement learning-based planning solutions, collaborate with engineers, and mentor junior