Research Engineer, Monetization AI Responsibilities

Develop and implement large-scale model architectures, leveraging model scaling and transfer learning techniques
Prioritize training scalability and signal scaling to optimize model performance, efficiency, and reliability
Develop and apply NextGen sequence learning techniques to drive advancements in recommender systems and machine learning
Design and implement generative modeling solutions for data augmentation
Develop and deploy machine learning pipelines
Collaborate with cross-functional teams to design and optimize ML systems, leveraging expertise in hardware-software co-design, including quantization, compression, and resource-efficient AI, to drive performance improvements and efficiency gains
Develop and implement innovative solutions for data-related challenges, utilizing knowledge of semi/self-supervised learning, generative techniques, sampling, debiasing, domain adaptation, continual learning, data augmentation, cold-start, 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
Exposure to architectural patterns of large scale software applications

Preferred Qualifications

PhD in AI, Computer Science, Data Science, or related technical fields
Master's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
First author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR, ICCV, CVPR, ACL, EMNLP, RecSys, KDD, WSDM, TheWebConf, ICDM, AAAI)
Direct experience in generative AI, LLMs, RecSys, ML research

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