Applied Researcher 5 (AI Foundations, LLM Core and Agentic AI)

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. Must not repeat job title. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate, navigation, legal, EEO, etc. Also remove application instructions. Must not create sections with filler like "commensurate with experience". Must only include sections if posting has real specific content. We need to parse the posting. Content: - Intro paragraph about Capital One AI, etc. Could be "About the role" maybe? Actually "About the role" is a heading for description of role. The posting includes: "At Capital One, we are creating trustworthy and reliable AI systems..." That's about the company and role. Could be "About the role". We'll put that in

. - Team Description: The AI Foundations team ... That's also about role/team. Could be part of About the role. - What You’ll Do: list of duties. That's Responsibilities. - The Ideal Candidate: bullet list of traits (Innovative, Creative, A leader, Technical...). That's maybe Skills or Qualifications? It describes ideal candidate traits. Could be "Skills" or "Qualifications". We'll decide. - Then "Basic Qualifications:" list. - "Preferred Qualifications:" list. - Then salary info: Pay section. - Benefits: mention "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits". That's Benefits. - Schedule: Not mentioned. No schedule info. So we should not include Schedule. - Also mention "This role is also eligible to earn performance based incentive compensation..." Could be part of Benefits or Pay? It's incentive compensation. Could be part of Benefits. We'll include in Benefits. - Also mention about accommodation etc. That's boilerplate legal; remove. - Also mention about agencies etc. Remove. - Also mention about equal opportunity etc. Remove. - Also mention about criminal history etc. Remove. - Also mention about technical support etc. Remove. - Also mention about third-party products etc. Remove. - Also mention about Capital One Financial entities. Remove. Thus sections: About the role (intro + team description), Responsibilities (What You’ll Do), Skills (Ideal Candidate traits), Requirements (Basic Qualifications), Qualifications (Preferred Qualifications), Pay (salary ranges), Benefits (benefits + incentive compensation). Possibly also "Qualifications" could be split into Requirements and Preferred Qualifications. We'll follow instruction: use headings for Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need Requirements (basic), Qualifications (preferred), Skills (ideal candidate traits). Benefits, Pay. Now we need to format lists: Use

• for any list of duties/requirements/benefits. For prose use

  . We must preserve ALL substantive information; do not invent or summarize away detail. So we need to include all details from those sections. Let's extract each. About the role: Combine intro paragraphs and Team Description. Intro: "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." Team Description: "Team Description: 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." We'll combine into a few paragraphs. Responsibilities (What You’ll Do): 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, 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. - Lead cross-functional research threads that bridge prototype model development and deployment with ML engineering partners, ensuring scientific insights translate to production impact. We'll put each as

• . Skills (Ideal Candidate): list of traits with descriptions. The text: "The Ideal Candidate: You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customersInnovative. 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 themCreative. 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 ideaA 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 beyondTechnical. 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 platformsHas a deep understanding of the foundations of AI methodologiesExperience 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 volumesExperience in delivering libraries, platform level code or solution level code to existing productsA professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projectsPossess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects" This is messy due to missing punctuation. We need to parse into bullet points. The original likely had bullet points: Innovative, Creative, A leader, Technical, Has a deep understanding..., Experience building..., An engineering mindset..., A professional..., Possess the ability... We'll extract each bullet with its description. Let's rewrite as 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 high quality 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. We'll keep as list. Requirements (Basic Qualifications): text: "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'll put as list? It's a single sentence with two options. Could be
  • ... or
  • ... but it's basically one requirement with alternatives. We'll keep as

    maybe but instruction says use

    • for any list of duties/requirements/benefits. This is a requirement, so we should use list. We'll break into two bullet points: - Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with the required degree obtained on or before the scheduled start date 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. We'll keep that. Qualifications (Preferred Qualifications): long list. We'll need to extract bullet points. The text: "Preferred Qualifications: PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fieldsLLMPhD focus on NLP or Masters with 5 years of industrial NLP research experienceMultiple publications 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)Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Publications in deep learning theory Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLROptimization (Training & Inference)PhD focused on topics related to optimizing training of very large deep learning models Multiple years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression Experience optimizing training for a 10B+ model Deep knowledge of deep learning algorithmic and/or optimizer design Experience with compiler designFinetuningPhD 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 labelingProven ability to design and lead small-scale research initiatives, guiding junior researchers or engineers through experimental design and evaluation" Again missing punctuation. We need to parse into bullet points. Likely categories: LLM, Optimization (Training & Inference), Finetuning, Data Preparation, Proven ability to design and lead small-scale research initiatives. Let's extract each bullet with its description. - PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields. - LLM PhD focus on NLP or Masters with 5 years of industrial NLP research experience. - Multiple publications 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). - Member of team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens). - Publications