Research Engineer, Monetization AI

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 next-generation sequence learning techniques to drive advancements in natural language processing and understanding Design and implement generative modeling solutions for data augmentation Research and develop graph-aware large language models Develop and deploy AutoML pipelines Apply Reinforcement Learning (RL) techniques, including long-term value optimization, RLHF, and RL4Reason Use causal learning to identify and understand the cause and effect of relationships across data 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