Gravis Robotics is a startup that turns heavy construction machines into intelligent and autonomous robots. Our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of machines in a gamified environment—from anywhere in the world. Our team has over a decade of academic experience honing the cutting edge of large-scale robotics, and is rapidly growing to bring that expertise into a trillion-dollar industry through active deployments with market leaders.
About the Job
We are looking for passionate, skilled interns with a background in machine learning to join our team—and to actively contribute to the development and deployment of extraordinary construction robots. The ideal candidate should be self-motivated, capable of working autonomously in a team and have a strong desire to solve exciting, challenging, and applied problems.
As part of the Autonomy team, you will focus on designing, testing, and benchmarking machine learning models, as well as conducting ablation studies to understand their performance and limitations. The insights generated through your work will help improve these models and support downstream applications, e.g. control policy synthesis, ultimately contributing to faster and more accurate machine digging.
What you will do
Design, test, and deploy novel ML models for autonomous heavy machinery
Benchmark and analyze model performance, including conducting ablation studies to evaluate key design choices.
Help define performance metrics, validation methodologies, and explore anomaly detection methods to understand the limitations of each architecture and which kind of data is needed for robust performance of the ML models.
Collaborate closely with the rest of the teams to improve the reliability and scalability of the ML pipeline.
Qualifications
Proficiency with Python, and Git.
Familiarity with popular deep learning libraries (PyTorch, etc.).
Experience with data analysis, ML optimization, and hyperparameter tuning.
Strong analytical and problem-solving skills, with the ability to interpret experimental results and draw sound conclusions.
Model-based reinforcement learning.
Large-scale robotics simulation environments, such as NVIDIA Isaac Sim.
Robot Operating System, including ROS or ROS 2.
C++.
The following experience is considered a plus:
This is an opportunity to join a dynamic and versatile team, and to be part of a young startup that will revolutionize heavy construction. As a forward-facing startup, we understand that work-life balance and flexibility are important considerations for many professionals: If you are a highly qualified candidate with the requisite skills and experience, we encourage you to apply and discuss your preferred working arrangement during the interview process.
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.
Skills Required
- Background in machine learning
- Proficiency with Python
- Proficiency with Git
- Familiarity with deep learning libraries (PyTorch, etc.)
- Experience with data analysis, ML optimization, and hyperparameter tuning
- Strong analytical and problem-solving skills; ability to interpret experimental results
- Self-motivated and capable of working autonomously in a team
- Proficiency in English
- Model-based reinforcement learning
- Large-scale robotics simulation environments (e.g., NVIDIA Isaac Sim)
- Robot Operating System (ROS or ROS 2)
- C++
What We Do
Developing autonomy for heavy machinery to automate an industry with a slowly rising productivity and a global labour shortage
