Applied Researcher 5 (AI Foundations - LLM, Optimization and Finetuning)
$263k - $300k • New York, NY • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
Capital One is building trustworthy and reliable AI systems to transform banking. We leverage machine learning for real-time, intelligent customer experiences and are committed to world-class applied science and engineering. The AI Foundations team is central to this vision, working across the research lifecycle from academia to production systems. You will partner with cross-functional teams to deliver AI-powered products, using technologies like Pytorch, AWS Ultraclusters, Huggingface, and Lightning to analyze vast amounts of data. This role involves building AI foundation models through all development phases, from design to implementation, and engaging in high-impact applied research to drive next-generation customer experiences.
Responsibilities
- Partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered products.
- Leverage technologies like Pytorch, AWS Ultraclusters, Huggingface, and Lightning to analyze large datasets.
- Build AI foundation models through all phases of development, including design, training, evaluation, validation, and implementation.
- Conduct high-impact applied research to advance customer experiences with the latest AI developments.
- Translate complex work into tangible business goals.
- Lead research threads bridging prototype development and deployment with ML engineering partners.
Requirements
- PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or MS with 2 years of applied research experience.
- Hands-on experience developing AI foundation models and solutions using open-source tools and cloud platforms.
- Deep understanding of AI methodologies.
- Experience building large deep learning models (language, images, events, or graphs).
- Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
- Engineering mindset with a track record of delivering models at scale.
- Experience delivering libraries, platform code, or solution code to existing products.
- Track record of high-quality ideas or improvements in machine learning, demonstrated by publications or projects.
- Ability to own and pursue a research agenda autonomously.
- PhD focus on NLP or Masters with 5 years of industrial NLP research experience.
- Multiple publications on pre-training of large language models.
- Experience training a large language model from scratch (10B+ parameters, 500B+ tokens).
- Publications in deep learning theory.
- Publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR.
- PhD focused on optimizing training of very large deep learning models.
- Experience and/or publications on model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression.
- Experience optimizing training for a 10B+ model.
- Deep knowledge of deep learning algorithmic and/or optimizer design.
- Experience with compiler design.
- PhD focused on guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning).
- Demonstrated knowledge of transfer learning principles.