Applied Researcher II
Not Disclosed•Full-TimeOn-site
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
About the Team
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. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking.
The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology, and business leaders to apply the state of the art in AI to our business, committed to building world-class applied science and engineering teams with breakthrough product experiences and scalable, high-performance AI infrastructure.
Work location
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
Key Responsibilities
- check_circlePartner with cross-functional teams to deliver AI-powered products
- check_circleBuild AI foundation models through design, training, evaluation, and implementation
- check_circleTranslate complex technical work into tangible business goals
- check_circleAnalyze numeric and textual data to reveal insights using PyTorch and AWS
- check_circleConduct high-impact applied research to advance customer experiences
Requirements
- verifiedPhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research
- verifiedM.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research
- verifiedExperience building large deep learning models
- verifiedExperience delivering libraries, platform level code or solution level code
- verifiedPublications in machine learning or deep learning theory
- verifiedWork authorization sponsorship available
Nice to Have
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, or Electrical Engineering. LLM focus on NLP or Masters with 5 years of industrial NLP research experience. Multiple publications on pre-training of large language models (e.g., technical reports, SSL techniques, optimization). Membership in a team that trained 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 focus on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series). Multiple papers on training models on graph and sequential data structures at KDD, ICML, NeurIPs, or ICLR. Experience 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 open-source frameworks (pytorch-geometric, DGL). Proposed new methods for inference or representation learning on graphs or sequences. Experience with datasets containing 100m+ users. PhD focused on optimizing training of very large deep learning models. Multiple years of 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, model adaptation, and model guidance. Experience deploying a fine-tuned large language model. Publications studying tokenization, data quality, dataset curation, or labeling. Contribution to a major open-source corpus. Contribution to open-source libraries for data quality, dataset curation, or labeling.
Benefits & Perks
check_circleEligibility for performance-based incentive compensation including cash bonuses and/or long-term incentivescheck_circleComprehensive health, financial, and other benefits supporting total well-beingcheck_circleEmployment authorization sponsorship consideration for qualified applicants
Similar Job Opportunities
Skills, education and keywords
Skills: Machine Learning, Ai, Pytorch, Aws, Huggingface, Lightning, Vector DBS, Deep Learning, LLM, NLP.
Education: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields required; Master's in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields with 4 years experience; PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields preferred.
Frequently asked questions about Applied Researcher II at Capital One
What does a Applied Researcher II at Capital One do?expand_more
Day-to-day, the Applied Researcher II at Capital One will partner with cross-functional teams to deliver ai-powered products; build ai foundation models through design, training, evaluation, and implementation; translate complex technical work into tangible business goals; and analyze numeric and textual data to reveal insights using pytorch and aws.
What are the requirements for this Applied Researcher II role?expand_more
To qualify for the Applied Researcher II at Capital One position, applicants should have: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research; M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research; Experience building large deep learning models; Experience delivering libraries, platform level code or solution level code; Publications in machine learning or deep learning theory; and Work authorization sponsorship available.
Where is the Applied Researcher II role at Capital One located?expand_more
Applied Researcher II at Capital One is based in NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States. This is a on-site role.
Is this Applied Researcher II job remote, hybrid, or on-site?expand_more
Capital One has listed this Applied Researcher II role as on-site.
How much experience is required for this Applied Researcher II role?expand_more
Applied Researcher II at Capital One typically requires 2–4 years of relevant experience at the mid level level.
What skills do you need for the Applied Researcher II role at Capital One?expand_more
Key skills for Applied Researcher II at Capital One include Machine Learning; Ai; Pytorch; Aws; Huggingface; Lightning; Vector DBS; and Deep Learning.
What education is required for Applied Researcher II at Capital One?expand_more
Educational requirements for this role: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields required; Master's in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields with 4 years experience; and PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields preferred.
What category does the Applied Researcher II role belong to?expand_more
Applied Researcher II at Capital One is part of the it job category on Recrutus.