Senior Software Engineer – MLOps
Amazon RIVR 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, Amazon RIVR is building the next generation of safe, reliable autonomous robots for last-mile delivery
What you’ll be doing
- Design and implement training pipelines for reinforcement learning policies.
- Work with AI and robotics engineers to streamline the model development lifecycle, including versioning and reproducibility.
- Implement ML monitoring and validation to ensure model performance in production.
- Support the automated deployment of AI models onto our robotic
- Create documentation and guidelines for best practices in MLOps.
- Stay up to date with the latest trends in MLOps and continuously improve our systems.
- Bachelor’s or higher degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in MLOps or a related field.
- Good knowledge of PyTorch.
- Experience with version control & CI/CD (Git, Github Actions, or similar)
- Familiarity with containerization (Docker, Kubernetes, etc.) for ML workloads.
- Experience with data and model versioning tools (e.g., DVC).
- Familiarity with the basic concepts of reinforcement learning.
- Master’s degree or higher.
- Familiarity with robotics platforms or ROS2.
- Experience with NVIDIA simulation tools (Isaac Sim, Isaac Lab).
- Experience applying machine learning infrastructure tooling (e.g. Ray, Hydra).
- Experience in a production environment with real-world hardware.
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