Applied Researcher II (AI Foundations)

We need to output HTML fragment with sections only if posting has real specific content. We must not repeat job title. Use

for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to extract content. From posting: Overview: description of Capital One AI, team description, "In this role, you will:" bullet list. Then "The Ideal Candidate:" bullet list of traits. Then "Basic Qualifications:" bullet list. Then "Preferred Qualifications:" bullet list. Then salary info. Then benefits description. Then other boilerplate to remove: EEO, accommodation, etc. We need to map to sections. We need to decide which sections to include: About the role (overview/team description), Responsibilities (the "In this role, you will:" list), Requirements (maybe Basic Qualifications?), Qualifications (maybe Preferred Qualifications?), Skills (maybe from Ideal Candidate?), Benefits, Pay, Schedule. We must not create sections with filler. Pay: we have salary ranges per location. That's specific content, so include Pay section. Schedule: not mentioned. No schedule info. So omit Schedule. Benefits: there is a paragraph about benefits: "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits..." That's specific enough. So include Benefits. Now we need to preserve all substantive information, not invent or summarize away detail. Must not omit details like bullet points. We need to format bullet lists as

• . For prose, use

  . We need to avoid repeating job title. No headings for Job ID etc. We need to output only HTML fragment. Let's craft sections:

  About the role

  Overview paragraph and Team Description combined? The overview includes: "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." Then 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 can combine into one

  or separate

  s. Probably separate paragraphs. Then Responsibilities: list from "In this role, you will:" bullet points. List items: - 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. We need to preserve dash etc. Keep as is. Then Requirements: maybe Basic Qualifications? The heading "Requirements" could be used for Basic Qualifications. We'll include Basic Qualifications content. Basic Qualifications bullet list: - 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 need to keep that as a single bullet? It's a long sentence with "or". We'll keep as one

• . Then Qualifications: Preferred Qualifications bullet list. Preferred Qualifications bullet list includes many items, each starting with a bold? Actually they are lines like: - 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 need to keep each as separate
• . Some lines are just headings like "Behavioral Models", "Finetuning", "Data Preparation". We'll keep them as items. Then Skills: from "The Ideal Candidate:" bullet list of traits. Ideal Candidate bullet list: - 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. We need to keep each bullet as
• . The bullet list includes sub-bullets? Actually each bullet starts with a trait (Innovative., Creative., A leader., Technical.) then sentences. We'll keep each as one
• containing the whole sentence(s). Might be long but okay. Then Benefits: paragraph about benefits. 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." We should keep that as

  . Might also include "Learn more at the Capital One Careers website." Keep. Then Pay: salary ranges. We need to present Pay section with details. Could be a

  containing the salary ranges per location. We must not include boilerplate like "This role is also eligible to earn performance based incentive compensation..." That's part of Pay? It's about incentives. Could be included in Pay section as it's about compensation. The instruction: include Pay section only if posting actually states them. It does state salary ranges and incentive compensation. So we can include that. We'll put:

  Pay

  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: $262,500 - $299,600 for Applied Researcher II McLean, VA: $262,500 - $299,600 for Applied Researcher II New York, NY: $286,400 - $326,800 for Applied Researcher II San Francisco, CA: $286,400 - $326,800 for Applied Researcher II San Jose, CA: $286,400 - $326,800 for Applied Researcher II 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.

  We should preserve line breaks? Not needed. Now we need to ensure we don't include sections like Schedule (no content). Also we should not include sections like Requirements if we used Basic Qualifications? The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to map content to those headings. We have: - About the role: overview + team description. - Responsibilities: the "In this role, you will:" list. - Requirements: Basic Qualifications. - Qualifications: Preferred Qualifications. - Skills: Ideal Candidate traits. - Benefits: benefits paragraph. - Pay: salary and incentive info. - Schedule: omit. We must not repeat job title. Good. Now ensure we don't include any boilerplate like EEO, accommodation, etc. We removed those. Check for any duplicated lines: we have not repeated. Now output HTML fragment. Let's craft. We'll need to escape any HTML special characters? The text includes em dashes — and maybe apostrophes. Should be fine as plain text. We'll output:

  About the role

  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,