Machine Learning Research Intern (Reinforcement/Imitation Learning)
- Vancouver, BC, Canada
Internship
Applications open in a new tab.
Job details
- Job type
- Internship
- Posting language
- English
- Posted
Original job description
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
More about this job
Responsibilities
You will design, implement, and improve state-of-the-art Reinforcement and Imitation Learning algorithms for humanoid robots. Additionally, you will develop training pipelines and collaborate with a multidisciplinary team to deploy these models in both simulation and physical environments.
Requirements
Candidates must be pursuing an MS or Ph.D. in Machine Learning, Computer Science, or a related field. You are expected to have hands-on experience with robotics, proficiency in Python, and familiarity with PyTorch or JAX.
Skills
- Reinforcement Learning
- Imitation Learning
- Robotics
- Python
- PyTorch
- JAX
- ROS2
- Computer Vision
- Simulation-to-reality transfer
- Robotic manipulation
- Machine learning
- Software development
- Algorithm design
- Data-collection pipelines
- Humanoid robotics
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Engineering
- Science & Research
- Software
- Data & Analytics
Keywords
- Machine Learning
- Reinforcement Learning
- Imitation Learning
- Robotics
- Physical AI
- Humanoid Robots
- Python
- PyTorch
- JAX
- ROS2
- Computer Vision
- Simulation
- Transfer Learning
- Robotic Manipulation
- Algorithm Development
- Data Collection
- Sample Efficiency
- Scalability
- R&D
- Production