Research Scientist Intern, AI for Photonics Inverse Design

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.We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.We have a special focus, for the present internship, on inverse design for photonics. Photonics are critical for opto-electronics, waveguides, optical fibers, and others, for an impact on wearable devices, silicon photonics, imaging and others. We need a free open source platform, integrating state of the art methods for inverse design, including RCWA and FDTD and robust optimization methods. The internship is on the AI part specifically, with interaction with experts on the electromagnetic simulation.We offer twelve (12) to twenty-four (24) weeks long internships and we have various start dates throughout the year.

Research Scientist Intern, AI for Photonics Inverse Design Responsibilities:

• Develop novel state-of-the-art Inverse design algorithms and corresponding systems, leveraging various artificial intelligence and deep learning techniques.
• Analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
• Perform state of the art research to advance the science and technology of Machine Learning and Artificial Intelligence, with impact on Inverse Design.
• Devise better data-driven models for inverse design, information retrieval, Multi-modal fusion, generation or media understanding (CV, NLP, Speech/ Audio).
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Publish research results and contribute to research that can be applied to Meta product development.

Minimum Qualifications:

• Currently has or is in the process of obtaining a Masters degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Reinforcement Learning, Mathematical Programming, or relevant technical field (or equivalent).
• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment.
• Experience with Python, C++, C, Java or other related language.
• Experience with deep learning frameworks such as Pytorch or Tensorflow.
• Experience building systems based on machine learning and/or deep learning methods.
• Research experience with algorithms for sequential decision-making, e.g., planning, reinforcement learning, or similar.

Preferred Qualifications:

• Intent to return to 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, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar.
• Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches.
• ML/ AI research and/ or work experience in information retrieval problems, generative approaches, and/ or Natural Language Processing, CV, or Speech/ Audio.
• Experience building systems based on machine learning, reinforcement learning and/or deep learning methods.
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
• Experience working and communicating cross functionally in a team environment.

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