AI Engineer - VLA Foundation Model, RIVR New

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.

- Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.

- At least 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 models or large-scale generalist robot models (e.g., RT-2, Octo).

- 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)

- 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++

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.