Senior AI Engineer - VLA Foundation Model

ph3Company Overview /h3 pAmazon 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. /p pFollowing 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. /p h3Position Overview /h3 pOur 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. /p h3Responsibilities /h3 ul liDevelop 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. /li liDesign, 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. /li liStreamline the data collection and training workflow to efficiently expand model capabilities with new tasks and data sources. /li liCollaborate with the reinforcement learning team to innovate methods that leverage both simulated and real‑world data. /li liOptimize and distill networks for real‑time deployment on the edge (e.g., Nvidia Jetson Thor). /li liBuild, lead and mentor an exceptional team of software engineers. /li liProvide expert guidance to product managers and executives for strategic decision‑making. /li liCreate and maintain documentation, guidelines, and best practices to streamline knowledge sharing. /li /ul h3Qualifications /h3 ul liMaster’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning. /li liA minimum of three years of industry or research experience, with PhD experience applicable. /li liStrong deep learning fundamentals including supervised learning, self‑supervised learning, Transformer‑based architectures, policy optimization algorithms, imitation learning, and generative AI techniques (including Diffusion Models). /li liProven experience in developing Vision‑Language‑Action (VLA) models or large‑scale generalist robot models (e.g., RT‑2, Octo, etc.). /li liStrong background in robotics including autonomy, navigation. /li liExperience with deploying artificial neural networks on hardware platforms. /li liAbility to prototype algorithms and train deep neural networks in Python (Pytorch). /li liPhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience. /li liPublications at top‑tier conferences. /li liExperience in managing a software team. /liliAbility to write production‑level code in modern C++. /li /ul h3Diversity Inclusion /h3 pAmazon RIVR is committed to building a diverse and inclusive team that values every perspective. If you’re passionate about driving innovation in robotics and creating meaningful impact, we encourage you to apply and bring your unique self to our team. /p h3Work Environment /h3 pWe believe the best work is done when collaborating and therefore require in‑person presence in our office locations. /p /p #J-18808-Ljbffr