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Zürich, Zürich, Schweiz/Suisse/Svizzera/Svizra
Onsite
Master's students looking to work full-time for an extended period (~6 months) in an invigorating environment where researchers and engineers are building the future of robotics infrastructure and systems. If you have a deep passion for writing clean, performant software and want to build the computational foundations that power advanced robotics, you will want to join us!
Prior robotics experience is not required—we are looking for exceptional software talent with strong fundamentals in system design and numerical or algorithmic computation. Depending on your background, your project will focus on one of two primary tracks: On-Robot Deployment or Off-Robot Training Infrastructure.
Track A: On-Robot Deployment
Write robust, low-latency modern C++ code for real-time execution directly on robotic hardware
Implement and optimize for embedded platforms like the Jetson family of devices
Profile, benchmark, and eliminate performance bottlenecks in resource-constrained Linux environments
Design and build clean software components using modern software engineering practices and ROS 2
Track B: Off-Robot Training Infrastructure
Design and build scalable software infrastructure for GPU based simulation and model training
Optimize parallel compute pipelines, rendering processes, and data ingestion to accelerate research cycles
Implement SOTA reinforcement learning algorithms to improve training performance
Currently pursuing a Master's (MS) degree in Robotics, Computer Science, Computational Science & Engineering, or a related field
Prior software engineering experience (e.g., industry internships, significant open-source contributions, or complex project work)
For On-Robot Deployment: Exceptional core software engineering foundations in Modern C++, system architecture, data structures, and memory management (C++ or another systems language is strictly required for this track)
For Off-Robot Infrastructure: Strong systems programming skills (C++, Python, etc.) with a deep interest in high-performance computing, distributed systems, or large-scale data processing. Experience with high level GPU programming languages (Warp, Jax) is a plus.
While strong core software engineering fundamentals in systems programming are required, no specific domain expertise beyond that is necessary. However, if you happen to possess experience in any single one of the following areas, it would be a massive plus and highly relevant to our current projects:
Hardware Acceleration & Graphics: Experience with GPU programming, CUDA, parallel processing, or rendering pipelines (e.g., Gaussian splatting).
Physics & Simulation: Familiarity with computational physics, contact models, rigid body dynamics, or physics engines (e.g., Nvidia Newton).
Machine Learning Systems: Experience optimizing or implementing embedded neural network inference and hardware-level ML acceleration.
Reinforcement Learning Algorithms: Experience working with and implementing RL algorithms from state of the art papers.