Applied Scientist, Amazon Robotics

Amazon Amazon · Big Tech · Sunnyvale, CA · Applied Science

Applied Scientist role focused on developing and training foundation models for robotics, integrating multi-modal learning, imitation learning, and reinforcement learning. The role involves model development, data management, experimentation, and research to enhance robotic perception and skill acquisition.

What you'd actually do

  1. Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments
  2. Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines.
  3. 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.
  4. Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs
  5. 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.

Skills

Required

  • PhD or Master's degree
  • 4+ years of building machine learning models or developing algorithms
  • 2+ years of deep learning
  • 2+ years of computer vision
  • 2+ years of human robotic interaction
  • 2+ years of algorithms implementation
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Programming in Java, C++, Python or related language
  • Experience in algorithms and data structures
  • Experience in parsing
  • Experience in numerical optimization
  • Experience in data mining
  • Experience in parallel and distributed computing
  • Experience in high-performance computing

Nice to have

  • Experience in state-of-the-art deep learning models architecture design
  • Experience in deep learning training and optimization
  • Experience in model pruning
  • Experience applying theoretical models in an applied environment

What the JD emphasized

  • PhD, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience
  • 2+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • foundation models
  • robotics
  • large vision-language models
  • reinforcement learning
  • imitation learning
  • multi-modal learning