About the role

Aplyr's Quick Take

This role is focused on developing systems that process and structure raw sensor data for training AI models in robotics. It's an individual contributor position where you'll work on self-supervised learning techniques to enhance data quality for various applications like navigation and decision-making.

Good fit

Ideal candidates have a strong background in AI, particularly in self-supervised learning and data engineering, with several years of experience in machine learning. A hands-on, problem-solving approach and the ability to collaborate with technical teams are essential.

Worth noting

The company is a relatively new spin-off from ETH Zurich, recently acquired by Amazon, which could mean a fast-paced environment with evolving priorities. The focus on real-world data processing in robotics is a unique challenge that may not suit everyone.

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

Job Description

Our global fleet of autonomous robots operates in the real world, generating vast amounts of multi-modal sensor data. While our VLA team focuses on building large-scale models to consume this data, much of it remains unlabeled and unstructured. We are seeking an expert in self-supervised and representation learning to unlock the full potential of this massive data pool.

In this role, you will be responsible for designing and building the core data engine that transforms raw, real-world sensor data into high-signal, structured datasets suitable for training neural networks. You will pioneer methods to automatically curate, filter, and pseudo-label this data, creating powerful representations that serve as the foundation for all downstream tasks, including navigation, imitation learning, and decision-making.

You will work directly with the VLA and Reinforcement Learning teams to define data strategies and interfaces, ensuring the data you produce directly accelerates their model development. If you are passionate about solving the "data bottleneck" in robotics and want to build the systems that learn meaningful patterns from the physical world, we invite you to join us.

Skills & Tags

ai data design

Aplyr's read

RIVR Media is at the forefront of media production, blending cutting-edge technology with creative content creation. Ideal for tech-savvy innovators and media enthusiasts.
Synthesized from recent postings & public sources

What's promising

•RIVR Media is known for its innovative approach to content creation across television and digital platforms.
•The company offers a diverse range of roles, indicating growth and opportunities for career advancement.
•RIVR Media's focus on technology integration in media production attracts top talent in engineering and AI.

What to watch

•The media production industry is highly competitive, posing challenges for sustained growth.
•Limited public information about RIVR's financial health and market position.
•Rapid technological changes require constant adaptation, which can be resource-intensive.

Why RIVR

•RIVR Media integrates advanced robotics and AI into its production processes, setting it apart from traditional media companies.
•The company hires for specialized roles like Robotics Platform Jetson Integration Engineer, reflecting its tech-driven focus.
•RIVR's emphasis on both hardware and software innovation in media production is relatively unique in the industry.

Aplyr’s read is generated by AI from public sources. Was it useful?