Applied Researcher I (AI Foundations)

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences.

• 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.

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields
• LLM
• PhD focus on NLP or 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
• 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 (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

• 401k matching
• Health insurance
• Flight privileges
• Long term incentives (LTI)

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