Applied Scientist, Robotics Evaluation & Data Systems

Amazon

New York (NY)

On-site

USD 100,000 - 145,000

Full time

14 days+

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Job summary

Amazon is seeking an Applied Scientist in New York to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this cross-functional role, you will focus on measuring, stress-testing, and improving robot behaviors across real-world tasks.

The ideal candidate should be able to design and implement evaluation policies, develop task definitions, and analyze data to enhance system performance. You will work at the intersection of robotics and machine learning, playing a critical role in shaping high-quality datasets and scalable data collection methods.

Qualifications

  • Strong experimental mindset with a systems-oriented approach.
  • Ability to work across software, robotics, and data pipelines.
  • Comfortable turning ambiguous goals into measurable evaluation systems.

Responsibilities

  • Design and implement evaluation frameworks for robots.
  • Develop success criteria and benchmarking methodologies.
  • Create high-quality data collection protocols.
  • Analyze evaluation results to identify performance gaps.
  • Collaborate with engineering teams for integration.
  • Stay updated on advances in robotics and evaluation.

Skills

Robotics
Machine Learning
Data Analysis
Evaluation Methodologies
Teleoperation
Data Pipelines

Job description

Amazon is seeking an Applied Scientist in New York to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this cross-functional role, you will focus on measuring, stress-testing, and improving robot behaviors across real-world tasks.

The ideal candidate should be able to design and implement evaluation policies, develop task definitions, and analyze data to enhance system performance. You will work at the intersection of robotics and machine learning, playing a critical role in shaping high-quality datasets and scalable data collection methods.

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