AI Software Engineer

Description

Meta is seeking a Software Engineer to join our team. The candidate is someone with experience working on maximizing performance of AI models on GPUs or custom silicon. This role involves applying these skills to solve some of the most crucial and exciting problems that exist on the web. The AI Applications Engineering team is dedicated to maximizing training and inference performance of Generative AI (GenAI) and Recommendation models on Meta's Training and Inference Accelerator (MTIA). We employ innovative optimization and parallelization strategies to maximize training throughput for the next generations of GenAI and recommendation models. Additionally, we work cross-functionally with many partner teams to ensure end-to-end performance of large-scale pre-training and inference, enabling us to deliver the next generation of AI experiences more quickly to our users.

Responsibilities

Apply in depth knowledge of AI infrastructure and hardware acceleration techniques to build and optimize our intelligent AI systems that improve Meta’s product and experiences Goal setting related to project impact, AI system design, and infrastructure efficiency Directly or influencing partners to deliver impact through thorough data analysis Drive large efforts across multiple teams Define use cases, and develop methodology & benchmarks to evaluate different approaches Apply in depth knowledge of how the AI infra interacts with the other systems around it

Qualifications

Bachelor’s degree in computer science or a related STEM field Specialized experience in one or more of the following AI/deep learning domains: AI infrastructure, hardware accelerators, high performance computing, AI compilers, performance optimizations, GPU architecture, on-device optimization, AI frameworks (PyTorch), HW/SW co-design and numerics Experience developing AI algorithms in C/C++ or Python for large-scale AI applications Master's degree/PhD in computer science or related STEM field and practical experience in AI development or accelerating AI models on AI accelerators Experience with distributed heterogeneous systems or on-device algorithm development Expertise in AI hardware accelerator architectures Experience with recommender and ranking models Experience in accelerating large AI models across multiple compute nodes Proven technical leadership experience