Senior Reinforcement Learning Engineer

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 is a startup turning heavy construction machines into intelligent and autonomous robots. Our unique combination of learning-based automation and augmented remote control enables a single operator to safely manage a fleet of earthmoving machines in a gamified environment. With over a decade of academic experience at the cutting edge of large-scale robotics, our team is rapidly translating this expertise into real-world deployments with industry leaders in a trillion-dollar market.

Our Rooftop Autonomous Control Kit (RACK) integrates sensing, compute, communication, and networking into a manufacturer-agnostic solution that works across a wide range of construction machines. We operate at the intersection of hardware, software, and real-world deployment, and we're growing fast.

About The Job

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.

The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. In this role, you will develop control modules designed to run across diverse machines, sites, and soil conditions. We are looking for a roboticist with a background in data-driven planning and/or control, strong Python skills, and a solid working knowledge of C++.

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.

What You Will Do

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 behavior of learned components

What We’re Looking For

We recognize that excellent candidates come from diverse backgrounds with various combinations of skills.

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 learningExperience 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.

You might be the perfect candidate for this or other positions. This is an opportunity to join a dynamic and versatile team, and to be part of a young startup that will revolutionize heavy construction.

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

As a forward-facing startup, we understand that work-life balance and flexibility are important considerations for many professionals:

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.