Applied Researcher II

Job Summary

Required Qualifications

• •Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date
• •Plus 2 years of experience in Applied Research
• •M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields
• •Plus 4 years of experience in Applied Research
• •Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms
• •Deep understanding of the foundations of AI methodologies
• •Experience building large deep learning models, whether on language, images, events, or graphs
• •Expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF
• •An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes
• •Experience in delivering libraries, platform level code or solution level code to existing products
• •A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects
• •The ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects
• •Experience with Pytorch
• •Experience with AWS Ultraclusters
• •Experience with Huggingface
• •Experience with Lightning
• •Experience with VectorDBs

Desired Qualifications

• •PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
• •LLM PhD focus on NLP
• •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
• •PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
• •Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR
• •Worked on scaling graph models to greater than 50m nodes
• •Experience with large scale deep learning based recommender systems
• •Experience with production real-time and streaming environments
• •Contributions to common open source frameworks (pytorch-geometric, DGL)
• •Proposed new methods for inference or representation learning on graphs or sequences
• •Worked datasets with 100m+ users
• •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
• •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
• •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