AI Engineer - VLA model, RIVR

Job description

RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software.

Our fleet of delivery robots operates globally today, generating vast amounts of robotic real-world data. By utilizing state-of-the-art Vision-Language-Action (VLA) models, large-scale generalist models (like Transformers), generative AI, and similar methods, we can leverage this pool of data to significantly enhance its autonomy, navigation, and manipulation skills. In this role, you will develop multi-modal models that enable robots to autonomously generate actions from demonstrations, real-time sensor data, and natural language commands.

We are seeking an expert in VLA models, imitation learning, and generative AI techniques with a deep knowledge of supervised, and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI we invite you to join us in shaping the future of intelligent robotics.

Key job responsibilities

Develop and implement 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.