Sr Director Applied Research
2 months ago
BasicQualifications
- Ph.D. plus at least 6 years ofexperience in Applied Research or M.S. plus at least 8 years ofexperience in Applied Research
- At least 5 yearsof people leadership experience
PreferredQualifications
- PhD in Computer Science, MachineLearning, Computer Engineering, Applied Mathematics, ElectricalEngineering or relatedfields
- LLM
- PhD focus on NLPor Masters with 10 years of industrial NLP researchexperience
- Core contributor to team that hastrained 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 thepre-training of large language models (e.g. technical reports ofpre-trained LLMs, SSL techniques, model pre-trainingoptimization)
- Has worked on an LLM (open sourceor commercial) that is currently available foruse
- Demonstrated ability to guide the technicaldirection of a large-scale model trainingteam
- Experience working with 500+ node clustersof GPUs Has worked on LLM scaled to 70B parameters and 1T+tokens
- Experience with common trainingoptimization frameworks (deep speed, nemo)
- BehavioralModels
- PhD focus on topics in geometric deeplearning (Graph Neural Networks, Sequential Models, MultivariateTime Series)
- Member of technical leadership formodel deployment for a very large user behaviormodel
- Multiple papers on topics relevant totraining models on graph and sequential data structures at KDD,ICML, NeurIPs, ICLR
- Worked on scaling graphmodels to greater than 50m nodes Experience with large scale deeplearning based recommender systems
- Experiencewith production real-time and streamingenvironments
- Contributions to common opensource frameworks (pytorch-geometric,DGL)
- Proposed new methods for inference orrepresentation learning on graphs orsequences
- Worked datasets with 100m+users
- Optimization(Training & Inference)
- PhD focused ontopics related to optimizing training of very large languagemodels
- 5+ years of experience and/orpublications on one of the following topics: Model Sparsification,Quantization, Training Parallelism/Partitioning Design, GradientCheckpointing, ModelCompression
- Finetuning
- PhDfocused on topics related to guiding LLMs with further tasks(Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning,Parameter Tuning)
- Demonstrated knowledge ofprinciples of transfer learning, model adaptation and modelguidance
- Experience deploying a fine-tunedlarge language model
- DataPreparation
- Numerous Publications studyingtokenization, data quality, dataset curation, orlabeling
- Leading contributions to one or morelarge open source corpus (1 Trillion +tokens)
- Core contributor to open sourcelibraries for data quality, dataset curation, orlabeling
CapitalOne will consider sponsoring a new qualified applicant foremployment authorization for this position
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