The Video ML Foundations team optimizes video ranking infrastructure to balance latency, cost, and freshness for an optimal user experience. We drive diverse initiatives, including co-designing ranking models and systems, accelerating model training, optimizing GPU inference, and building funnel infrastructure and elastic compute. We are looking for candidates with an infrastructure background eager to apply systems and optimization methodologies to ranking systems. Our core focus is advancing state-of-the-art AI, ML, and RecSys technologies—spanning ranking, retrieval, model architecture, and optimization—to achieve long-term product goals.
Responsibilities
- Develop and implement large-scale model architectures, leveraging model scaling and optimization techniques
- Collaborate with cross-functional teams to design and optimize ML systems, leveraging expertise in hardware-software co-design, including quantization, kernels, and resource-efficient AI, to drive performance improvements and efficiency gains
- Develop and implement innovative solutions for data-related challenges, utilizing knowledge of supervised learning, generative techniques, sampling, reinforcement learning, content understanding, and large language models
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Research experience in machine learning, deep learning, natural language processing, and/or recommender systems
- Experience with developing machine learning models at scale from inception to business impact
- Programming experience in Python and hands-on experience with frameworks such as PyTorch
- Experience with architectural patterns of large-scale software applications First author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR, ICCV, CVPR, ACL, EMNLP, RecSys, KDD, WSDM, TheWebConf, ICDM, AAAI)
- PhD in AI, Computer Science, Data Science, or related technical fields
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Direct experience in generative AI, LLMs, RecSys, ML research
- Master's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies