Reality Labs Research is looking for an intern to help us develop the next generation assistance systems that guide the users in contextual and adaptive future AR/VR systems. In particular, we are seeking candidates who have experience with either of the following: multimodal learning, self-supervised learning, video understanding, representation learning.Work with researchers to help enable their work across the following research disciplines: - AI for Egocentric Representation Learning - Multimodal Learning Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year. Some projects may require a minimum of 16 consecutive weeks.

Research Scientist Intern, AI for Egocentric Representation Learning (PhD) Responsibilities:

• Develop, implement, and evaluate methods for learning robust representations from multi-modal egocentric data (e.g., video, audio, inertial measurement units).
• Make use of Meta's large infrastructure to scale and speed up experimentation.
• Write modular research code that can be reused in other contexts.
• Collaborate with other researchers.
• Work towards taking on big problems and deliver clear, compelling, and creative solutions to solve them at scale.
• The work should result in publishable research to appear in a top-tier ML or CV conference (e.g., NeurIPS, ICLR, CVPR, ECCV).

• Currently has or is in the process of pursuing a PhD in machine learning, computer vision, speech processing, applied statistics, computational neuroscience, or relevant technical field.
• Excellent research skills involving defining problems, exploring solutions, and analyzing and presenting results.
• Proficiency in python and machine learning libraries (numpy, scikit-learn, scipy, pandas, matplotlib, tensorflow, pytorch).
• Deep understanding of at least one of the following areas: Transfer, few-shot, zero-shot, continual and/or online learning, self-supervised learning, or multi- or cross-modal learning.
• Interpersonal skills: cross-group collaboration and cross-culture collaboration.
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.

• 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, CHI, UIST, IMWUT, CVPR, ICCV, ECCV, AAAI, ICRA, SIGGRAPH, ETRA, or similar.
• Experience with deep metric learning / neural net embedding methods.
• Experience on vision based input recognition systems, such as hand tracking, body pose estimation.
• Experience on working with time sequence form sensor data, such as IMU and audio.
• Experience working and communicating cross functionally in a team environment.
• Intent to return to degree program after the completion of the internship/co-op.
• Availability for minimum 16 consecutive week internship.