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    Sr Director Applied Research - Dubai, United Arab Emirates - SimplyApply

    SimplyApply
    SimplyApply Dubai, United Arab Emirates

    6 days ago

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    Description

    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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    BasicQualifications · Currently has, or is in the processof obtaining, a PhD, with an expectation that required degree willbe obtained on or before the scheduled start date or M.S. with atleast 2 years of experience in Applied Research · Preferred Qualifications [choosecorrect se ...

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