Senior Reinforcement Learning Engineer

Gravisrobotics; Zürich; Vor Ort; CHF 120.000 - 170.000; Vollzeit

Zusammenfassung

Gravis Robotics in Zurich, Switzerland is building autonomous systems for heavy machinery, combining learning-based planning with real-world deployment. You will develop data-driven control modules that operate across machines and sites, addressing diverse soils and sim2real challenges.

This role requires hands-on experience with real robots, strong Python (PyTorch) and C++, and the ability to debug complex robotic systems in production environments.

Qualifikationen

• 2–5 years in RL for control/planning on real robots
• GPU-accelerated simulators experience (IsaacSim/IsaacLab, CARLA, MuJoCo)
• Strong Python skills; PyTorch or similar
• Proficiency in C++
• Comfortable debugging real-world system behavior
• Willingness to travel as required

Aufgaben

• Develop data-driven planning and control systems for autonomous excavation across machines and soils
• Improve simulations to reduce the sim2real gap
• Define data collection pipelines for real data in policy training
• Design experiments for continuous performance and robustness improvements
• Explore adaptive and online reinforcement learning in deployed systems
• Provide mentorship 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 behaviour of learned components

Kenntnisse

Reinforcement learning for control

Python

C++

Debugging real-world systems

Travel willingness

Tools

PyTorch

IsaacSim/IsaacLab

CARLA

MuJoCo

Jobbeschreibung

Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry, turning heavy construction machines into autonomous robots.

Gravis began as an ETH Zurich spin-out, and our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment.

Backed by deep robotics research and now deployed across multiple countries with leading construction and equipment partners, our team is rapidly growing to bring this technology to a trillion-dollar industry.

The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. You will build control modules that run on many different machines, across many sites, with different soil conditions. We’re looking for a roboticist with data driven planning and/or control background, deep python expertise and good level of C++ proficiency.

To be successful in this role you should have experience working with real robots, tackling the challenges of sim2real transfer, and deploying robotic systems in a production environment.

To thrive in this role, you should have experience working with physical robots, navigating the challenges of sim-to-real (sim2real) transfer, and deploying robotic systems into production environments.

Learning-Based Planning and Control for Real Systems

• 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.

System Integration

• Integrate learned components into a larger software stack

• Collaborate with excavation and motion planning engineers

• Build tools for analysing and evaluating the behaviour of learned components

Core qualifications

• 2-5 years industry experience developing Reinforcement learning systems for control and/or planning and deploying them on real robots with a customer. If you only have experience with simulation, you’re most likely not a good fit for this position.

• 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++

• Comfortable debugging real-world system behavior

• Ability and willingness to travel as required by business projects.

Great-to-Have Skills & Experience

• Experience with hydraulic machinery

• Experience with supervised learning or imitation learning

• Research experience in reinforcement learning

• Experience deploying robotic systems at scale (e.g. hundreds of units)

• Familiarity with ROS or similar robotics frameworks

• Experience with feature-flagged deployments, staged rollouts, or long-lived platforms

• Experience with data curation for ML applications

• Experience guiding, mentoring, or leading junior colleagues, students, or project teams.

• Familiarity with or interest in utilizing AI coding tools.

This Role is a Great Fit If

• You are passionate about building systems that work reliably in the real world

• You want to help build a long-lived excavation planning and control system intended to scale and positively impact the entire construction industry.

• You are comfortable working with the realities of imperfect data and noisy measurements.

• You have a keen interest in bridging the sim2real gap and understanding the differences between simulation and physical environments.

• You are excited to help drive technical direction in a growing team transitioning from prototyping to the product stage.

• You value a collaborative team culture rooted in thoughtful design, creative thinking, mutual respect, and pragmatism.

Gravis Robotics offers a fair market salary and a working location in the vibrant city of Zurich.

Gravis is an equal opportunity employer.

We are committed to building an inclusive and diverse team, and do not discriminate based upon race, color, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics.

We are an international team that is working to solve problems with a global impact: to facilitate efficient communication and collaboration, proficiency in English is a requirement for all roles.