Research Scientist Intern, AI Systems Machine Learning (PhD) - Meta

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 Fundamental AI Research (FAIR). 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 solving systems challenges in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.The mission of Meta FAIR's SysML research is to advance the state of AI through open science innovations. We explore, design, and build AI systems and infrastructures at scale to enable cutting-edge AI technologies.Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

RESPONSIBILITIES

Research Scientist Intern, AI Systems Machine Learning (PhD) Responsibilities:

• Perform state of the art research to advance the science and technology of machine learning systems.
• Perform 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, memory and energy-efficient AI systems, environmentally-sustainable AI system and hardware designs.
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Publish research results and contribute to research that impacts Meta product development.

MINIMUM QUALIFICATIONS

Minimum Qualifications:

• Currently has or is in the process of obtaining a Ph.D. degree in Machine Learning, Systems, Artificial Intelligence, or 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 PyTorch framework.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

PREFERRED QUALIFICATIONS

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, 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.