AppliedResearcher(AIFoundations,LLMCustomization,Finetuning,ReinforcementLearning)

PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields, LLM, PhD focus on NLP or Masters with 5 years of industrial NLP research experience, Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization), Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens), Publications in deep learning theory, Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR, Optimization (Training & Inference), PhD focused on topics related to optimizing training of very large deep learning models, Multiple years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression, Experience optimizing training for a 10B+ model, Deep knowledge of deep learning algorithmic and/or optimizer design, Experience with compiler design, Finetuning, PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning), Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance, Experience deploying a fine-tuned large language model, Data Preparation, Publications studying tokenization, data quality, dataset curation, or labeling, Contribution to a major open source corpus, Contribution to open source libraries for data quality, dataset curation, or labeling