Applied Scientist II, Reinforcement Learning

The Robot Age; Reading, Northern (MA, KY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Amazon Robotics is seeking an applied scientist in North Reading, MA to advance reinforcement learning for robotic systems, including dexterous manipulation, locomotion and human-robot interaction, through simulation and real-world deployment.

The role emphasizes technical research with outcomes that inform production robotics, ensuring learned behaviors remain robust in dynamic environments and safe around humans.

Qualifications

• Graduate-level experience in ML, robotics, controls, or a related field.
• Experience with imitation learning and reinforcement learning for whole-body control.
• Familiarity with model-predictive control, simulation environments, or related methods.
• Ability to build research systems that can inform production robotics.

Responsibilities

• Develop reinforcement learning methods for robotic control and behavior
• Work on imitation learning, whole-body control, and simulation-driven training
• Connect learned policies with manipulation, locomotion, and HRI problems
• Evaluate robustness as systems move from simulation toward real deployment

Skills

Reinforcement learning

Imitation learning

Whole-body control

Robotics research

Education

Graduate-level degree in ML/Robotics/Controls

Tools

Simulation environments

Model-predictive control

Job description

What the role actually is

Amazon Robotics is hiring an applied scientist in North Reading for reinforcement learning on advanced robotic systems. The listing points to dexterous manipulation, locomotion, human-robot interaction, simulation, and control.

The role is technical research, but it is relevant to product-facing robotics because it deals with learned behaviors that need to be robust in dynamic environments and safe around humans.

What you would work on

• Develop reinforcement learning methods for robotic control and behavior
• Work on imitation learning, whole-body control, and simulation-driven training
• Connect learned policies with manipulation, locomotion, and HRI problems
• Evaluate robustness as systems move from simulation toward real deployment

What they are asking for

• Graduate-level experience in ML, robotics, controls, or a related field
• Experience with imitation learning and reinforcement learning for whole-body control
• Familiarity with model-predictive control, simulation environments, or related methods
• Ability to build research systems that can inform production robotics

Why this one is worth a look

Amazon's scale makes robot learning unusually consequential: small improvements can affect large fleets and many human workflows. This role is worth tracking because it connects reinforcement learning to deployed automation rather than purely lab benchmarks.