- PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
- LLM
- PhD focus on NLP or Masters with 10 years of industrial NLP research experience
- Core contributor to team that has trained 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 the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization)
- Has worked on an LLM (open source or commercial) that is currently available for use
- Demonstrated ability to guide the technical direction of a large-scale model training team
- Experience working with 500+ node clusters of GPUs Has worked on LLM scaled to 70B parameters and 1T+ tokens
- Experience with common training optimization frameworks (deep speed, nemo)
- Behavioral Models
- PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
- Member of technical leadership for model deployment for a very large user behavior model
- Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR
- Worked on 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 common open source frameworks (pytorch-geometric, DGL)
- Proposed new methods for inference or representation learning on graphs or sequences
- Worked datasets with 100m+ users
- Optimization (Training & Inference)
- PhD focused on topics related to optimizing training of very large language models
- 5+ years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression
- 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
- Data Preparation
- Numerous Publications studying tokenization, data quality, dataset curation, or labeling
- Leading contributions to one or more large open source corpus (1 Trillion + tokens)
- Core contributor to open source libraries for data quality, dataset curation, or labeling
- Recognized authority in AI or ML research with demonstrated influence on the evolution of a subfield (e.g., foundation models, multimodal learning, reasoning systems, or model efficiency)
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
Cambridge, MA: $350,000 - $399,500 for Sr. Staff Applied Researcher
McLean, VA: $350,000 - $399,500 for Sr. Staff Applied Researcher
New York, NY: $381,800 - $435,700 for Sr. Staff Applied Researcher
San Francisco, CA: $381,800 - $435,700 for Sr. Staff Applied Researcher
San Jose, CA: $381,800 - $435,700 for Sr. Staff Applied Researcher
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
