Senior Director, Applied Research
COMFORT SYSTEMS, Mc Lean, VA, United States
Senior Director, Applied Research
Capital One is creating trustworthy and reliable AI systems to reimagine banking. The AI Foundations team is at the center of this mission, building world‑class applied science and engineering teams and scalable AI infrastructure. As a people manager, you will lead cross‑functional teams of scientists, engineers, and product managers to deliver AI‑powered products that change how customers interact with their money.
Key Responsibilities
Partner with 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—including PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and VectorDBs—to reveal insights hidden in large volumes of numeric and textual data.
Build AI foundation models through all phases of development—design, 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.
Translate the complexity of your work into tangible business goals through effective interpersonal communication.
Ideal Candidate
Passionate about designing and creating AI solutions while doing the right thing for customers.
Innovative, staying current on published state‑of‑the‑art methods and evaluating emerging technologies.
Creative and comfortable tackling undefined problems, asking questions, and pursuing new ideas.
A people leader who challenges conventional thinking, develops talent, and collaborates with stakeholders.
Technically proficient with open‑source languages and cloud computing platforms, with hands‑on experience building AI foundation models.
Has a deep understanding of AI methodologies and has built large deep‑learning models (language, images, events, or graphs).
Experienced in training optimization, self‑supervised learning, robustness, explainability, or RLHF.
Engineering mindset demonstrated by delivering models at scale in terms of training data and inference volumes.
Delivered libraries, platform‑level code, or solution‑level code to existing products.
Has published first‑author papers or led projects that produced new machine‑learning ideas.
Owns and pursues a research agenda, selecting impactful problems and executing long‑running projects autonomously.
Basic Qualifications
PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of applied research experience, or equivalent M.S. plus 8 years of applied research experience.
At least 5 years of people‑leadership experience.
Preferred Qualifications
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 a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens).
Numerous publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on large‑language‑model pre‑training or related topics.
Experience with an LLM (open‑source or commercial) that is currently available for use.
Guided the technical direction of a large‑scale model training team.
Experience with 500+ node GPU clusters and high‑scale LLM training (70B parameters, 1T+ tokens).
Proficient with deep‑speed, NeMo, or similar training optimization frameworks.
Experience with behavioral models, graph neural networks, sequential models, or multivariate time series.
Member of technical leadership for production of a large user‑behavior model.
Seventy‑million‑node graph model scaling experience or large‑scale recommender‑system training.
Experience in real‑time streaming production environments.
Contributions to open‑source frameworks such as PyTorch‑Geometric or DGL.
Developed new inference or representation‑learning methods for graphs or sequences.
Worked with datasets containing 100M+ users.
Specialized in training optimization, model sparsification, quantization, partitioning, gradient checkpointing, or compression.
Focused on fine‑tuning large models (supervised, instruction, dialogue, parameter tuning).
Knowledge of transfer learning, model adaptation, and guidance principles.
Deployed a fine‑tuned large language model.
Focused on data preparation, tokenization, data quality, labeling, or dataset curation.
Contributed to or led large open‑source corpora (1 trillion+ tokens) or tools for data quality.
Capital One is an equal‑opportunity employer (EOE, including disability and veteran status) committed to non‑discrimination.
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Key Responsibilities
Partner with 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—including PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and VectorDBs—to reveal insights hidden in large volumes of numeric and textual data.
Build AI foundation models through all phases of development—design, 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.
Translate the complexity of your work into tangible business goals through effective interpersonal communication.
Ideal Candidate
Passionate about designing and creating AI solutions while doing the right thing for customers.
Innovative, staying current on published state‑of‑the‑art methods and evaluating emerging technologies.
Creative and comfortable tackling undefined problems, asking questions, and pursuing new ideas.
A people leader who challenges conventional thinking, develops talent, and collaborates with stakeholders.
Technically proficient with open‑source languages and cloud computing platforms, with hands‑on experience building AI foundation models.
Has a deep understanding of AI methodologies and has built large deep‑learning models (language, images, events, or graphs).
Experienced in training optimization, self‑supervised learning, robustness, explainability, or RLHF.
Engineering mindset demonstrated by delivering models at scale in terms of training data and inference volumes.
Delivered libraries, platform‑level code, or solution‑level code to existing products.
Has published first‑author papers or led projects that produced new machine‑learning ideas.
Owns and pursues a research agenda, selecting impactful problems and executing long‑running projects autonomously.
Basic Qualifications
PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of applied research experience, or equivalent M.S. plus 8 years of applied research experience.
At least 5 years of people‑leadership experience.
Preferred Qualifications
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 a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens).
Numerous publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on large‑language‑model pre‑training or related topics.
Experience with an LLM (open‑source or commercial) that is currently available for use.
Guided the technical direction of a large‑scale model training team.
Experience with 500+ node GPU clusters and high‑scale LLM training (70B parameters, 1T+ tokens).
Proficient with deep‑speed, NeMo, or similar training optimization frameworks.
Experience with behavioral models, graph neural networks, sequential models, or multivariate time series.
Member of technical leadership for production of a large user‑behavior model.
Seventy‑million‑node graph model scaling experience or large‑scale recommender‑system training.
Experience in real‑time streaming production environments.
Contributions to open‑source frameworks such as PyTorch‑Geometric or DGL.
Developed new inference or representation‑learning methods for graphs or sequences.
Worked with datasets containing 100M+ users.
Specialized in training optimization, model sparsification, quantization, partitioning, gradient checkpointing, or compression.
Focused on fine‑tuning large models (supervised, instruction, dialogue, parameter tuning).
Knowledge of transfer learning, model adaptation, and guidance principles.
Deployed a fine‑tuned large language model.
Focused on data preparation, tokenization, data quality, labeling, or dataset curation.
Contributed to or led large open‑source corpora (1 trillion+ tokens) or tools for data quality.
Capital One is an equal‑opportunity employer (EOE, including disability and veteran status) committed to non‑discrimination.
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