Applied Scientist, Vision for Robotic Perception

Socket.dev; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon Robotics is seeking a hands-on Applied Scientist to deepen vision systems across robotic platforms, including edge deployment and real-time perception capabilities. The role emphasizes leadership, collaboration with cross-functional teams, and translating research into scalable products.

The team focuses on 3D perception, large-scale learning, and robust robotics deployments in fulfillment environments, with opportunities to influence long-term roadmaps and drive end-to-end ownership.

Qualifications

• PhD or Master’s degree and 4+ years of CS, CE, ML or related field experience.
• Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.

Responsibilities

• Architect, design, and implement Machine Learning models for vision systems on robotic platforms.
• Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and roadmap.
• Work with stakeholders across science and operations teams to iterate on design and implementation.
• Maintain high standards by participating in reviews and designing for fault tolerance and operational excellence.
• Prototype and test concepts or features, both through simulation and with live robotic equipment.
• Mentor other engineer team members.

Skills

Java

C++

Python

Deep learning

Computer vision

ML algorithms

Education

MS/PhD in CS/ML/CE

Tools

TensorFlow

PyTorch

Job description

Amazon Robotics is seeking a hands-on Applied Scientist to deepen vision systems across robotic platforms, including edge deployment and real-time perception capabilities. The role emphasizes leadership, collaboration with cross-functional teams, and translating research into scalable products.

The team focuses on 3D perception, large-scale learning, and robust robotics deployments in fulfillment environments, with opportunities to influence long-term roadmaps and drive end-to-end ownership.