Applied Researcher II (AI Foundations)

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to parse the posting. We have Overview, Team Description, In this role you will: (list of duties). The Ideal Candidate: (list of traits). Basic Qualifications, Preferred Qualifications, salary info, benefits statement, etc. We need to map to sections: - About the role: maybe overview and team description? Could combine. - Responsibilities: the bullet list under "In this role, you will:" (Partner with..., Leverage..., Build..., Engage..., Flex...). Those are duties. - Requirements: maybe Basic Qualifications? Or "Requirements" could be basic qualifications. - Qualifications: maybe Preferred Qualifications? Or we could have both Requirements and Qualifications. - Skills: maybe technical skills list from Ideal Candidate? Or from Preferred Qualifications? Could be separate. - Benefits: there is a paragraph about benefits: "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits..." Also mention performance based incentive compensation. - Pay: salary ranges per location. - Schedule: not mentioned; maybe not include. We must not include sections with placeholder or vague content. So we need to include only sections with concrete info. Let's extract: Overview: At Capital One, we are creating trustworthy and reliable AI systems... (prose). Team Description: The AI Foundations team is at the center... (prose). Could combine into About the role. Responsibilities: bullet list: - 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. - Flex your interpersonal skills to translate the complexity of your work into tangible business goals. Requirements: Basic Qualifications: - Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research We could format as list. Qualifications: Preferred Qualifications: list many items. Could be long; we need to preserve all substantive info, not summarize. Must include each bullet. Preferred Qualifications list includes many items grouped under categories: Behavioral Models, Finetuning, Data Preparation etc. Actually the text: "Preferred Qualifications: PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fieldsBehavioral ModelsPhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)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 with datasets with 100m+ usersFinetuningPhD 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 guidanceExperience deploying a fine-tuned large language model Data PreparationPublications 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" It seems missing punctuation; we need to parse into bullet points. We'll need to split by capitalized section headings? The text runs together. We need to preserve all substantive info; we can break into logical bullets based on the topics. Let's try to extract each bullet: - PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields - Behavioral Models: PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) - 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 with datasets with 100m+ users - 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: 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 We'll keep as list. Skills: maybe from Ideal Candidate: Innovative, Creative, A leader, Technical. Could be considered skills. But we already have qualifications; maybe we can put Ideal Candidate traits under Skills. However the instruction says prefer few substantive sections over many thin ones. We could combine Ideal Candidate into Skills section. Let's extract Ideal Candidate bullet points: - Innovative: You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them. - Creative: You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea. - A leader: You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond. - Technical: You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms. Has a deep understanding of the foundations of AI methodologies. Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF. An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes. Experience in delivering libraries, platform level code or solution level code to existing products. A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects. Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects. That's a lot; we could keep as list. Benefits: paragraph: "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 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." We'll keep as prose. Pay: salary ranges per location list. We need to output as

headings and

• for lists,

  for prose. We must not include sections with placeholder like "commensurate with experience". Not present. We must not include Schedule (no info). So omit. We must not include Job ID etc. We must not repeat job title as heading. We must output only HTML fragment. Let's structure:

  About the role

  Overview paragraph + Team Description paragraph (maybe combine). We'll put two paragraphs? Could be one

  containing both sentences. Better: two

  tags? The rule: use

  for prose. Could have multiple

  elements. We'll do:

  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. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.

  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.

  Then Responsibilities:

  Responsibilities

  • 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.
  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
  Requirements (Basic Qualifications):

  Requirements

  • Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research
  • Or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research
  Qualifications (Preferred Qualifications):

  Qualifications

  • PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
  • Behavioral Models: PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
  • 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 with datasets with 100m+