Machine Learning Research Intern (Reinforcement/Imitation Learning)

Sanctuary; Vancouver; Presencial; CAD 20.000 - 31.000; A tiempo parcial

Descripción de la vacante

Sanctuary AI is seeking an intern to join the ML team, contributing to perception, planning, and motion systems for humanoid robots.

You will tackle RL and IL challenges, bridge simulation to real-world deployment, and gain deep exposure to the platform architecture powering our general-purpose robots.

Formación

• Pursuing MS or Ph.D. in Machine Learning, Computer Science, Applied Math, or related field.
• Hands-on experience in RL and IL methods with robotics focus.
• Experience deploying ML models on robotics platforms.
• Experience in sim-to-real transfer and perception, planning, and motion for humanoid robots.

Responsabilidades

• Design, implement, and benchmark RL/IL methods for robot manipulation.
• Integrate ML models into robotics software stacks and simulators.
• Collaborate with RL Lead and multi-disciplinary teams to improve data pipelines and deployment.
• Communicate research progress and identify promising directions for the team.

Conocimientos

Python

PyTorch

JAX

ROS2

RL/IL foundations

Educación

MS/PhD candidate in ML/CS/Applied Math

Herramientas

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Descripción del empleo

Your New Role and Team

Sanctuary AI, a world leader in building dexterity-driven Physical AI for general purpose robots, is looking to hire a skilled and motivated intern to join our ML team and contribute to our team’s work in engineering and innovating unique robotic manipulation tasks.

Reporting to the RL Lead, you will have the opportunity to tackle a variety of challenges related to the perception, planning, and motion systems for humanoid general-purpose robots.

The best candidates for this role have excellent software development and machine learning skills as well as hands-on experience with robotics. In this role you’ll be a valued contributor and you’ll gain a comprehensive understanding of the design, architecture, and implementation of the simulation platform and machine learning systems that power our general-purpose robots.

We are flexible regarding both the internship start date and the duration of the term.

Our Success Criteria

• Design, implement, and improve state-of-the-art Reinforcement Learning (RL) and Imitation Learning (IL) algorithms and test them in real-world settings

• Keep up to date with state-of-the-art RL/IL methodologies and robotics

• Identify, communicate, and drive promising research directions to the team

• Find ways of improving existing implementations of RL/IL pipelines with regards to standard metrics such as sample efficiency, speed, computational resource usage, and scalability

• Design RL/IL training and data-collection pipelines to facilitate fast deployment on physical robots

• Work with a multidisciplinary team to develop novel algorithms and investigate sources of errors with existing implementations
  

Your Experience

Desired Qualifications

• Pursuing MS or Ph.D. in Machine Learning, Computer Science, Applied Math, or related field

• Experience implementing a variety of RL and IL methods with a focus in a specialization such as computer vision or robotics

• Hands-on experience integrating ML models onto a robotics platform

• Experience implementing and deploying (dexterous) robotic manipulation tasks in simulation and on physical robots

• Experience taking ML R&D and trained models into production

• Experience with computer vision systems

• Experience in simulation-to-reality transfer learning

Skills

• Development with Python 3.6 or later

• Working knowledge of PyTorch and/or JAX

• Familiarity with ROS2

• Extensive knowledge of RL/IL principles and use

Traits

• Above all else, a consistently positive attitude and a willingness to do whatever it takes to create robust solutions to complex problems

• Optimistic listening and conflict resolution capabilities

• Demonstrated ability to influence others without authority

• Eager to take on new challenges with tenacity and positivity

• Patience, persistence, and attention to detail when resolving performance issues

• Obsession with bringing human-like intelligence to machines