Sr Director Applied Research

4 weeks ago


dubai, United Arab Emirates SimplyApply Full time

Basic Qualifications

  • Ph.D. plus at least 6 years of experience in Applied Research or M.S. plus at least 8 years of experience in Applied Research
  • At least 5 years of people leadership experience

Preferred Qualifications

  • PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
  • LLM
    • PhD focus on NLP or Masters with 10 years of industrial NLP research experience
    • Core contributor to team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)
    • Numerous publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR 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)
    • Has worked on an LLM (open source or commercial) that is currently available for use
    • Demonstrated ability to guide the technical direction of a large-scale model training team
    • Experience working with 500+ node clusters of GPUs Has worked on LLM scaled to 70B parameters and 1T+ tokens
    • Experience with common training optimization frameworks (deep speed, nemo)
  • Behavioral Models
    • PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
    • Member of technical leadership for model deployment for a very large user behavior model
    • 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
  • Optimization (Training & Inference)
    • PhD focused on topics related to optimizing training of very large language models
    • 5+ years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression
  • 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
    • Numerous Publications studying tokenization, data quality, dataset curation, or labeling
    • Leading contributions to one or more large open source corpus (1 Trillion + tokens)
    • Core contributor to open source libraries for data quality, dataset curation, or labeling

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position

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