AI Research Scientist, SysML - FAIR at Meta

Boston, Massachusetts, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Jun, 26

Salary

257000.0

Posted On

14 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning Systems, Distributed Training, Training Library, cuBLAS, cuDNN, FlashAttention, Hardware-Software Co-design, Data Semantics, Scalable Machine Learning Systems, Resource-Efficient AI, Neural Network Architectures, Memory-Efficient AI, Energy-Efficient AI, Data-Driven Models, Python, C++

Industry

Software Development

Description

Meta is seeking Research Engineers 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 who are experienced in solving systems challenges to sustainably accelerate our reach to human-level intelligence. Candidates will have an opportunity to make fundamental advances in systems 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 ML systems and infrastructures at scale with usability, efficiency, and sustainability as design principles. Some aspects of this role include enabling distributed training at an unprecedented scale through advancements and development in training library and authoring components, such as cuBLAS, cuDNN, FlashAttention, training performance acceleration through hardware-software co-design. Responsibilities Carry out cutting-edge 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) 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 Devise better data-driven models of AI system design and optimization 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 Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD degree in Computer Science, Computer Engineering, a relevant technical field, & 2+ years of equivalent domain-specific industry experience Development experience in systems, computer architectures, compiler and programming languages, machine learning, and artificial intelligence Experience with Python, C++, C, Rust or other related languages and with PyTorch framework Experience developing and optimizing systems for at-scale machine learning execution 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 solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward Experience working and communicating cross functionally in a team environment Preferred Qualifications Proven track record of achieving significant results and publications as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as MLSys, ISCA, ASPLOS, HPCA, PLDI, CGO, NeurIPS, ICML, ICLR, or similar Demonstrated research and software engineering experience via work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub) $184,000/year to $257,000/year + bonus + equity + benefits

Responsibilities

The role involves conducting cutting-edge research to advance machine learning systems science and technology, focusing on enabling learning the semantics of various data modalities. Responsibilities include contributing research innovations in scalable ML systems, resource efficiency, and sustainable AI system designs.