IT Process Manager using AI to improve process landscape for packaging machinery. Collaborating on AI use cases and managing their implementation in projects.
About the role
- Applied Researcher I utilizing AI foundations to enhance customer banking experiences at Capital One. Collaborating with cross-functional teams to build and implement innovative AI-powered solutions for improved interactions.
Responsibilities
- Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.
- Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
- Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
- Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
- Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
Requirements
- Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields.
- M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research.
- PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields preferred.
- LLM PhD focus on NLP or Masters with 5 years of industrial NLP research experience preferred.
- Multiple publications on topics related to the pre-training of large language models.
- 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.
- Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance.
- Experience deploying a fine-tuned large language model.
Benefits
- Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
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