Applied Scientist Ii, Campaign and Creative

Amazon Amazon · Big Tech · IN, HR, Gurugram · Applied Science

This role focuses on building and deploying machine learning models for computer vision systems on robotic platforms, specifically for automotive shopping experiences. It involves end-to-end solution delivery, from design and implementation to optimization and deployment on the edge, with a strong emphasis on deep learning and computer vision techniques.

What you'd actually do

  1. Architect, design, and implement Machine Learning models for vision systems on robotic platforms
  2. Optimize, deploy, and support at scale ML models on the edge.
  3. Work with stakeholders across , science, and operations teams to iterate on design and implementation.
  4. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement.
  5. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment

Skills

Required

  • Java
  • C++
  • Python
  • deep learning
  • machine learning
  • computer vision
  • machine learning techniques
  • algorithms and data structures
  • parsing
  • numerical optimization
  • data mining
  • parallel and distributed computing
  • high-performance computing

Nice to have

  • Unix/Linux
  • professional software development

What the JD emphasized

  • building scalable computer vision machine learning systems
  • deep learning
  • semi supervised learning
  • dynamic learning
  • computer vision
  • machine learning techniques
  • vision systems on robotic platforms
  • ML models on the edge

Other signals

  • build new discovery and shopping products
  • design, and build innovative automotive-shopping experiences
  • design, implement and deliver end-to-end solutions
  • building scalable computer vision machine learning systems
  • deep learning, semi supervised learning and dynamic learning
  • Architect, design, and implement Machine Learning models for vision systems on robotic platforms
  • Optimize, deploy, and support at scale ML models on the edge
  • Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment