Applied Science Manager, Amazon Industrial Robotics

Amazon•North Reading, MA

About The Position

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial 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 - Enable unprecedented robustness and reliability, industry-ready - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration 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.

Requirements

Experience leading a science team
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning
6+ years of building machine learning models for robotics including LLM, VLM, VLA / LBM and Reinforcement Learning
PhD and 6+ years of applied research experience
Experience in software engineering and business applications

Nice To Haves

Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.

Responsibilities

Build and lead a team of scientists and developers responsible for foundation model development
Define the right ‘FM recipe’ to reach industry ready solutions
Define the right strategy to ensure fast and efficient development, combining state of the art methods, research and engineering.
Lead 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
Lead Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines.
Lead 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.
Lead 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.