Facebook Video Ml Foundation Team Role The Facebook Video ML foundation team is focusing on building ML and ranking foundations for Facebook video recommendation systems. In this role, you will work on optimizing the e2e stack for model training and inference for large scale recommendation models, with opportunities coming from the domains of distributed systems, model/system co-design, GPU optimizations, and more. While the core of day-to-day work and key responsibility will be to identify and lead the execution for short/mid term opportunities for efficiency optimization, you will also drive long term strategies and shape team direction on things like model/system co-design, enablement for new model paradigms and unblock model iterations for better recommendation experience. Required Skills: Software Engineer, Infrastructure Responsibilities: Identify performance opportunities and bottlenecks across a wide range of Facebook video recommendation models, infrastructure and systems Implement changes to capture efficiency improvements Help other engineers both inside and outside the team to execute on efficiency and performance opportunities, issues and bottlenecks Minimum Qualifications: 2+ years of programming experience in a relevant programming language 2+ years relevant experience building large-scale infrastructure systems or similar experience 1+ year of experience identifying, designing and completing medium to large features independently without guidance Experience with scripting languages such as Python, Javascript or Hack Experience building and shipping high quality work and achieving high reliability Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta Preferred Qualifications: Exposure to architectural patterns of large scale software applications Experience in programming languages such as C, C++, Java Hands-on experience with large-scale ML infra systems (for example, GPU training clusters) Experience in training and/or inference solutions for large models (e.g. recommendation models or LLMs) Experience in high performance computing including communication optimization, CUDA kernel optimization, distributed training and inference, etc