Research Scientist Intern, AI Systems Machine Learning (PhD)

Meta - Boston, MA

Full-time - Intern

Boston, MA

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About the position

Meta is seeking Research Interns to join the Fundamental AI Research (FAIR) team, specifically for the Research Scientist Intern position focused on AI Systems and Machine Learning. This internship is designed for individuals who are passionate about solving complex systems challenges in various domains such as deep learning, computer vision, audio and speech processing, natural language processing, and machine learning. The mission of Meta FAIR's SysML research is to advance the state of AI through open science innovations, exploring, designing, and building AI systems and infrastructures at scale to enable cutting-edge AI technologies. Interns will have the opportunity to perform state-of-the-art research aimed at advancing the science and technology of machine learning systems. This includes conducting research that enables the understanding of data semantics across multiple modalities, such as images, video, text, and audio. Interns will also be tasked with devising improved data-driven models for AI system design and optimization, contributing to innovations in scalable machine learning systems, resource-efficient AI data and algorithm scaling, and memory and energy-efficient AI systems. Additionally, the role involves collaborating with researchers and cross-functional partners, effectively communicating research plans, progress, and results, and publishing research findings that impact Meta's product development. The internship duration ranges from twelve (12) to twenty-four (24) weeks, with various start dates available throughout the year. This position offers a unique opportunity to make core algorithmic advances and implement ideas at an unprecedented scale, contributing to the future of AI technologies.

Responsibilities

• Perform state of the art research to advance the science and technology of machine learning systems.
• Conduct research that enables learning the semantics of data (images, video, text, audio, and other modalities).
• Devise better data-driven models of AI system design and optimization.
• Contribute research that leads to innovations in scalable machine learning systems, resource-efficient AI data and algorithm scaling, and neural network architectures.
• Develop memory and energy-efficient AI systems and environmentally-sustainable AI system and hardware designs.
• Collaborate with researchers and cross-functional partners, communicating research plans, progress, and results.
• Publish research results and contribute to research that impacts Meta product development.

Requirements

• Currently has or is in the process of obtaining a Ph.D. degree in Machine Learning, Systems, Artificial Intelligence, or a relevant technical field.
• Research experience in systems, computer architectures, compiler and programming languages, machine learning, and artificial intelligence.
• Experience with Python, C++, C, Lua, or other related languages and with the PyTorch framework.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

Nice-to-haves

• 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, MLSys, ISCA, ASPLOS, CGO, PLDI, PACT, HPCA, MICRO, or similar.
• Experience developing and optimizing systems for at-scale machine learning execution.
• Experience in real-system implementations.
• Experience devising data-driven models and real-system experiments and design implementation for AI system optimization.
• Experience with scalable machine learning systems, resource-efficient AI data and algorithm scaling, or neural network architectures.
• Experience with memory and energy-efficient AI systems, environmentally-sustainable AI system designs, or AI-driven system optimization.
• Experience solving analytical problems using quantitative approaches.
• Ability to manipulate and analyze complex, large scale, high-dimensionality data from varying sources.
• Experience in utilizing theoretical and empirical research to solve problems.
• Experience building systems based on machine 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.