About the job
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.
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
Qualifications
Minimum
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 d
Preferred
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)