Robotics Applied Science Manager, Manipulation & AI

Socket.dev; Arlington (VA); On-site; USD 183,800 - 248,700; Full time

Job summary

Amazon Robotics seeks an Applied Science Manager to lead a team of scientists and engineers in advanced manipulation and perception for fulfillment center automation. You will shape technical visions, define priorities, and partner with cross‑functional teams to deliver scalable solutions.

Based in Arlington, VA, you will oversee research programs, guide model development for grasp learning and foundation models, and drive system architecture and best practices to ship high‑quality robotics

Qualifications

• PhD or MS with strong quantitative background and research track record.
• 6+ years of applied research experience in robotics or ML.
• 3+ years of managers experience leading scientists/engineers.
• Experience programming in Java, C++, Python or related language.
• Knowledge of full lifecycle engineering practices and live-site operations.
• Experience building ML models or algorithms for business use cases.
• Experience hiring and growing top talent.
• Strong understanding of robotics, CV and ML (Grasping, 3D geometry, CNNs/transformers).

Responsibilities

• Define a science and engineering strategy and product features.
• Drive system architecture for scalable robotics solutions.
• Lead and develop a team of scientists and engineers.
• Collaborate with partners across teams to deliver automation for robots.

Skills

Machine learning

Robotics

Computer vision

Leadership

Java

C++

Python

Education

PhD in engineering / CS / ML or related field

MS + 4+ years industry/academia research

Tools

Git / source control

Job description

Amazon Robotics seeks an Applied Science Manager to lead a team of scientists and engineers in advanced manipulation and perception for fulfillment center automation. You will shape technical visions, define priorities, and partner with cross‑functional teams to deliver scalable solutions.

Based in Arlington, VA, you will oversee research programs, guide model development for grasp learning and foundation models, and drive system architecture and best practices to ship high‑quality robotics