Auf einen Blick
- Aufgaben: Entwickle autonome Systeme für Bagger in realen Baustellenumgebungen.
- Unternehmen: Gravis Robotics, ein innovatives Startup im Bereich Robotik.
- Vorteile: Wettbewerbsfähiges Gehalt, flexible Arbeitszeiten und Entwicklungsmöglichkeiten.
- Weitere Informationen: Dynamisches Team mit Mentoring-Möglichkeiten für Junioren und Praktikanten.
- Warum dieser Job: Gestalte die Zukunft der Robotik und arbeite an spannenden Projekten.
- Qualifikationen: 2-5 Jahre Erfahrung in der Entwicklung von Reinforcement Learning Systemen.
Das prognostizierte Gehalt liegt zwischen 63000 - 77000 € pro Jahr.
Gravis Robotics is a startup that turns heavy construction machines into autonomous robots.
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.
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 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.
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.
If you meet most of the core qualifications below, we highly encourage you to apply.
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.
Isaac Sim/Isaac Lab, CARLA, Mu Jo Co) Strong Python skills and experience with Py Torch 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
Reinforcement Learning Engineer Arbeitgeber: Gravis Robotics
Gravis Robotics ist ein innovatives Start-up, das in der pulsierenden Stadt Zürich ansässig ist und sich auf die Entwicklung intelligenter und autonomer Baumaschinen spezialisiert hat. Wir bieten eine dynamische Arbeitsumgebung, in der Teamarbeit und Kreativität gefördert werden, sowie zahlreiche Möglichkeiten zur beruflichen Weiterentwicklung. Unsere Mitarbeiter profitieren von flexiblen Arbeitszeiten und einer ausgewogenen Work-Life-Balance, während sie an spannenden Projekten arbeiten, die einen globalen Einfluss haben.
