The Role
Assist the AI engineering team in developing and implementing reinforcement learning algorithms for robotic systems while gaining hands-on experience in software integration and testing.
Summary Generated by Built In
RIVR is a Swiss robotics company pioneering Physical AI and robotic solutions to revolutionize last-mile delivery, giving 1 human the power of 1000. Through the combination of artificial neural networks and innovative robot designs with wheels and legs, RIVR aims to enhance efficiency, sustainability, and scalability in last-mile delivery. Founded as Swiss-Mile, the company rebranded to RIVR in 2025 to better reflect its mission of driving the future of intelligent robotics.
As a Reinforcement Learning Intern, you will assist the AI engineering team in developing and implementing reinforcement learning algorithms for robotic systems. Additionally, your responsibilities include extending the software stack to evaluate our algorithms in simulation and on hardware. This role offers hands-on experience in algorithm development, software integration, and testing within a dynamic and collaborative environment. We are committed to finding and nurturing exceptional talent; our internships are a key pathway to recruiting outstanding graduates who can make a significant impact in our team.
Important Notice: For this position, we can unfortunately only accept applications from citizens of Schengen Area countries. This restriction does not apply to ETHZ and EPFL students who are required to complete compulsory internships as part of their studies.
What you’ll be doing
- Support the team in developing, debugging, and testing reinforcement learning algorithms for robotic applications.
- Work closely with senior engineers to integrate software with hardware components and contribute ideas to enhance system performance.
- Engage in continuous learning and gain exposure to recent developments from the reinforcement learning literature
What you must have
- At least BSc in Robotics, Engineering, Computer Science or equivalent.
- Previous coursework or projects related to reinforcement learning or learning-based control.
- Proficiency in Python and at least one common deep learning framework, such as PyTorch, Jax or TensorFlow.
- Eagerness to learn and contribute in a team environment.
- Strong problem-solving and analytical skills.
Get some bonus points
- Proven experience implementing reinforcement learning algorithms.
- Familiarity with modern simulation frameworks for reinforcement learning, such as IsaacSim.
RIVR is committed to building a diverse and inclusive team that values every perspective. If you’re passionate about driving innovation in robotics and creating meaningful impact, we encourage you to apply and bring your unique self to our team. We believe the best work is done when collaborating and therefore require in-person presence in our office locations.
RIVR is committed to building a diverse and inclusive team that values every perspective. If you’re passionate about driving innovation in robotics and creating meaningful impact, we encourage you to apply and bring your unique self to our team.
We believe the best work is done when collaborating and therefore require in-person presence in our office locations.
Skills Required
- At least BSc in Robotics, Engineering, Computer Science or equivalent
- Previous coursework or projects related to reinforcement learning or learning-based control
- Proficiency in Python and at least one common deep learning framework
- Strong problem-solving and analytical skills
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The Company
What We Do
Reality in Virtual Reality Limited is a developer of Virtual Reality assets in both 360 video and photo realistic virtual reality experiences. Offering immersive training for all industries. We scan any real-world environment and use our RiVR VR Simulation Engine and our VRM (Virtual Reality Monitor) to enable cutting edge training anywhere in the world. With our simulation engine we can capture any location and recreate it in photorealistic virtual reality. RiVR allows users to interact with and experience these worlds, enhancing the way humans learn.
