Robot Perception Scientist (Applied Scientist II)

Amazon; Sunnyvale (CA); On-site; USD 172,000 - 222,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking an Applied Scientist in Robot Perception to develop and deploy state-of-the-art perception algorithms for robotic systems, enabling object detection, segmentation, depth estimation and scene understanding. You will bridge research and real-world impact across teams facing bold challenges in AI, robotics and vision.

You will mentor engineers, publish findings, and contribute to a culture of technical excellence while advancing perception capabilities at scale in dynamic

Qualifications

• PhD in engineering, technology, computer science, ML, robotics or equivalent.
• 2+ years of building ML models for business applications.
• Publications in top-tier venues or journals.

Responsibilities

• Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding
• Lead research initiatives in computer vision, sensor fusion and 3D perception
• Collaborate with cross-functional teams to define and deliver perception capabilities
• Drive end-to-end ownership of ML models—from data labeling to training, evaluation, deployment
• Mentor junior scientists and engineers; contribute to a culture of technical excellence
• Publish research findings in top venues and contribute to patents

Skills

Java

C++

Python

PyTorch

JAX

Computer Vision

Education

PhD in engineering / CS / robotics

Tools

Hadoop

Spark

Job description

Amazon is seeking an Applied Scientist in Robot Perception to develop and deploy state-of-the-art perception algorithms for robotic systems, enabling object detection, segmentation, depth estimation and scene understanding. You will bridge research and real-world impact across teams facing bold challenges in AI, robotics and vision.

You will mentor engineers, publish findings, and contribute to a culture of technical excellence while advancing perception capabilities at scale in dynamic