AI Engineer - VLA Foundation Model, RIVR
AI in this role
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.
Basic qualifications
- 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)
Preferred qualifications
- 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++
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How we rate this
AI Engineer - VLA Foundation Model, RIVR at Amazon rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used PyTorch hands-on?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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