Applied Scientist

Reading Massachusetts 01867

Job Description
Applied Scientist II Reinforcement Learning Industrial Robotics Group (L5) Location: MA

Job description

Our client is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at their scale. They are building revolutionary robotic systems that combine cutting-edge AI sophisticated control systems and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale working with world-class teams pushing the boundaries of whats possible in robotic dexterous manipulation locomotion and human-robot interaction.

This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. Our client leverages advanced robotics machine learning and artificial intelligence to solve complex operational challenges at an unprecedented scale. Their fleet of robots operates across hundreds of facilities worldwide working in sophisticated coordination to fulfill our mission of customer excellence.

The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team thats revolutionizing how robots learn adapt and interact with their environment. Join them in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration.


Key job responsibilities

Design and implement whole body control methods for balance locomotion and dexterous manipulation
Utilize state-of-the-art in methods in learned and model-based control
Create robust and safe behaviors for different terrains and tasks
Implement real-time controllers with stability guarantees
Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation
Mentor junior engineer and scientists


Basic qualifications

PhD or Masters degree and 2 years of applied research experience
Experience with imitation learning and reinforcement learning for whole-body control
Experience with methods such as hierarchical quadratic programming and modelpredictive control
Experience with simulation environments such as IsaacLab Mujoco Drake etc.
Experience with developing and deploying code for real-time controllers
Experience in state estimation from multiple sensor modalities


Preferred qualifications

Experience programming in Java C Python or related language
Experience working effectively across cross-functional teams and partnering well with people at all levels within an organization
PhD in Robotics with a focus on whole-body control
Experience with low-level joint torque/impedance control
Experience with teleoperation systems
Experience with robotics frameworks for fast prototyping (Matlab ROS etc.)

Job Responsibilities
Design and implement whole body control methods for balance locomotion and
dexterous manipulation
Utilize state-of-the-art in methods in learned and model-based control
Create robust and safe behaviors for different terrains and tasks
Implement real-time controllers with stability guarantees
Collaborate effectively with multi-disciplinary teams to co-design hardware and
algorithms for loco-manipulation
Mentor junior engineer and scientists