Research Scientist Intern, Foundational Models for Robotics Research (PhD)

Meta; Menlo Park (CA); On-site; USD 78,000 - 100,000; Full time

Job summary

Meta is seeking Research Interns for Reality Labs in the Robotics Research team. Internships run 12–24 or 24 weeks with various start dates.

You will develop novel generative AI algorithms and collaborate with researchers across teams to push the boundaries of AI and robotics. You will work on projects that analyze and train models across images, video, 3D, text, and audio modalities, and you will contribute to research that can be applied to Meta products.

Qualifications

• PhD in CS/AI/Robotics or related field.
• Proven research track record with publications or grants.

Responsibilities

• Develop state-of-the-art generative AI algorithms for robotics.
• Improve efficiency, scalability, and stability of deployed algorithms.
• Perform research to advance intelligent machines.

Skills

Python

C++

C

Machine learning

Deep learning

Research

Multimodal learning

Education

Ph.D. in Computer Science/AI/Robotics

Tools

PyTorch

TensorFlow

Job description

About

Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.Meta is seeking Research Interns to join our Reality Labs in the Robotics Research team. We are committed to advancing the fields of artificial intelligence and robotics by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as generative modeling, reinforcement learning, robotics, and foundational models. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

• Develop novel state-of-the-art generative AI algorithms and corresponding systems, leveraging various deep learning techniques to robotics.
• Based on the project, help analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
• Perform research to advance the science and technology of intelligent machines.
• Perform research that enables learning the semantics of and training generative models of data (images, video, 3D, text, audio, and other modalities).
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Disseminate research results.
• When applicable, contribute to research that can be applied to Meta product development.

Minimum Qualifications

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Generative AI, Robotics, or relevant technical field
• Experience with Python, C++, C, or other related languages
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• Experience building systems based on machine learning and/or deep learning methods Intent to return to the degree program after the completion of the internship/co-op
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, or similar
• Experience working and communicating cross functionally in a team environment
• Experience in advancing AI techniques, including core contributions to open source libraries and frameworks
• Publications or experience in machine learning, deep learning, multimodal learning, generative modeling, or robotics
• Experience solving analytical problems using quantitative approaches
• Experience setting up ML experiments and analyzing their results
• Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources
• Experience in utilizing theoretical and empirical research to solve problems
• Experience with deep learning frameworks