Applied Scientist, Amazon Robotics

About the role

Amazon Robotics is seeking an exceptional Applied Scientist to join our Foundation Models and Agentic AI team. This role presents an opportunity to shape the future of robotics through innovative applications of large vision-language-action (VLA) models, reasoning and task planning models, and reinforcement learning.

At Amazon Robotics, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence.

We are pioneering the development of robotics foundation models that:

- Enable unprecedented generalization across diverse tasks

- Integrate multi-modal learning capabilities (visual, depth, force, tactile, linguistic)

- Accelerate policy skill acquisition through imitation learning and reinforcement learning

- Enhance robotic perception and environmental understanding

- Streamline development processes through reusable capabilities

The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment.

Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration.

Key job responsibilities

As an Applied Scientist in the Applied AI and Robotics Foundations Model team, you will:

- Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments

- Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines.

- Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance.

- Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs

- Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions.

- Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.