Senior AI Engineer - VLA Foundation Model
RIVR, part of Amazon is a robotics company pioneering Physical AI through real-world doorstep delivery. Founded in 2024 as an ETH Zurich spin-off, RIVR developed wheeled-legged robots designed to operate in complex, unstructured environments such as stairs, gates, doors, and uneven urban terrain. We believe that achieving general physical intelligence requires solving real customer problems in the real world, where robots can learn from rich operational data at scale.
Following our acquisition by Amazon in March 2026, we are continuing this mission with greater reach and speed. By combining custom robot hardware, onboard autonomy, and cloud-based coordination, RIVR, part of Amazon is building the next generation of safe, reliable autonomous robots for last-mile delivery
What you’ll be doing
- Develop and implement cutting-edge Vision-Language-Action (VLA) models, generalist robot transformers, and imitation learning algorithms (e.g., diffusion policies) to enable robots to autonomously execute complex tasks.
- Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and navigation tasks, with a focus on spatial reasoning and generalization.
- Streamline the data collection and training workflow to efficiently expand model capabilities with new tasks and data sources.
- Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data.
- Optimize and distill networks for real-time deployment on the edge (e.g. Nvidia Jetson Thor).
- Build, lead and mentor an exceptional team of software engineers.
- Provide expert guidance to product managers and executives for strategic decision-making.
- Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.
What you must have
- Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
- A minimum of three years of industry or research experience, with PhD experience applicable.
- Strong deep learning fundamentals including supervised learning, self-supervised learning, Transformer-based architectures, policy optimization algorithms, imitation learning, and generative AI techniques (including Diffusion Models).
- Proven experience in developing Vision-Language-Action (VLA) models or large-scale generalist robot models (e.g., RT-2, Octo, etc.).
- Strong background in robotics including autonomy, navigation.
- Experience with deploying artificial neural networks on hardware platforms.
- Ability to prototype algorithms and train deep neural networks in Python (Pytorch)
Get some bonus points
- PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
- Publications at top-tier conferences.
- Experience in managing a software team.
- Ability to write production-level code in modern C++
Skills
As published by lever · 12 questions · 2 written answers
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, GitHub URL, Portfolio URL
Short answers (4)
- Nationality
- Earliest Start Date (DD.MM.YYYY)
- Nationality
- Earliest starting date (DD.MM.YYYY)
Pick from a list (2)
- Swiss Residence Permit optional
- Swiss residence permit optional
Written answers (2)
- Can you provide an example of a project where you implemented imitation learning or a VLA model on actual hardware? Please specify the hardware system used, the method implemented, and the problem it solved, sharing any permissible details.
- What potential advantages do you see in applying multi-modal Vision-Language-Action (VLA) models and spatial reasoning to enhance the capabilities of an autonomous wheeled-legged robo? Discuss possible applications and benefits, particularly regarding generalization and zero-shot navigation.
Attachments (4)
- Cover Letter
- Other Supporting Documents (e.g. grade transcripts or reference letters) optional
- Cover letter
- Other supporting documents (e.g. grade transcripts or reference letters) optional