Research Scientist Intern, Gen AI Scientific Capabilities (PhD)
Meta

Job Info

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 Generative AI efforts. We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world, and equally committed to ensuring that the models and products we release meet our high standards for safety. We are seeking individuals passionate in areas related to improving the state of the art for AI safety, with a focus on LLMs. This work will encompass a broad set of workstreams dedicated to understanding how LLMs can support scientific workflows, including development of new algorithms, tool integrations, and evaluations that help push our understanding forward. Our interns have an opportunity to make advances both in core algorithms and tools that drive the impact of LLMs for scientific domains such as biology and chemistry, as well as to lead the field in ensuring the safety of LLM deployments - and of course, to apply their ideas at an unprecedented scale.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Gen AI Scientific Capabilities (PhD) Responsibilities:

• Develop novel state-of-the-art methods for assessing and improving LLM capabilities in science, with a particular focus on high-impact domains such as biology and chemistry
• Develop industry-leading methods that improve the robustness and safety of deployed models deployed models
• Perform research to advance the science and technology of intelligent machines
• Disseminate research results
• Publish research results and contribute to research that can be applied to Meta product development
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results

Minimum Qualifications:

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Computer Vision, Audio Processing, Artificial Intelligence, Generative AI, Computational Biology, Computational Chemistry, or relevant technical field
• Research experience in machine learning, deep learning, synthetic biology, bioinformatics, computational chemistry, and/or natural language processing
• Experience with Python, C++, C, Java or other related languages
• Experience with deep learning frameworks such as Pytorch, Tensorflow, or JAX
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment

Preferred Qualifications:

• 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, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, Cosyne, or similar
• Experience working and communicating cross functionally in a team environment
• Publications or experience in machine learning, AI, computer vision, optimization, computer science, statistics, applied mathematics, data science, computational biology, or computational chemistry
• 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
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)

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