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)
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.
