Applied Researcher 4 (AI Foundations, LLM Core and Agentic AI)
for section headings. Use - for any list of duties/requirements/benefits, and
for prose.
We need to decide which sections to include: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay. Possibly combine Requirements and Qualifications? The instruction says use those headings if content exists. We have Basic Qualifications and Preferred Qualifications. Could treat them as Requirements (basic) and Qualifications (preferred). Also we have a list of traits under "The Ideal Candidate:" which could be Skills. Or we could treat them as Skills.
Let's map:
- About the role: introductory paragraphs + Team Description.
- Responsibilities: "What You’ll Do:" bullet list.
- Requirements: Basic Qualifications.
- Qualifications: Preferred Qualifications.
- Skills: The Ideal Candidate bullet list (traits) maybe also include technical skills like experience with PyTorch etc? Those are in responsibilities but could also be skills. But we can keep Skills as the Ideal Candidate traits.
- Benefits: benefits paragraph.
- Pay: salary info + incentive compensation.
We must not include sections with filler like "commensurate with experience". Not present.
We need to preserve all substantive info. So we need to include all details from each section.
Let's extract content.
About the role:
Paragraph 1: "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: "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."
Combine into maybe two paragraphs.
Responsibilities (What You’ll Do):
- 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.
- Collaborate with senior researchers to prepare internal tech reports or conference submissions summarizing novel methods or findings.
We need to keep the dash punctuation? Use plain text.
Requirements (Basic Qualifications):
- Currently has, or is in the process of obtaining, a 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 or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research.
We could keep as a paragraph or list. Since it's a single item, we could use
. But instruction: use
- for any list of duties/requirements/benefits. If it's a single item, still could be list. We'll use
- .
Qualifications (Preferred Qualifications): many bullet points. Need to preserve all.
Let's extract each bullet:
- 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 in deep learning theory
- Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
- Optimization (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 design
- 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
- Demonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks
- Experience designing controlled experiments and documenting reproducibility results for internal or public research
We need to keep the structure. Could flatten but preserve details. Use nested lists? The instruction only says use
- for any list. Could have nested lists. We'll do top-level ul for each major category, with li containing sub-ul if needed.
Skills (The Ideal Candidate): bullet list of traits:
- 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
for prose. We need to decide which sections to include: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay. Possibly combine Requirements and Qualifications? The instruction says use those headings if content exists. We have Basic Qualifications and Preferred Qualifications. Could treat them as Requirements (basic) and Qualifications (preferred). Also we have a list of traits under "The Ideal Candidate:" which could be Skills. Or we could treat them as Skills. Let's map: - About the role: introductory paragraphs + Team Description. - Responsibilities: "What You’ll Do:" bullet list. - Requirements: Basic Qualifications. - Qualifications: Preferred Qualifications. - Skills: The Ideal Candidate bullet list (traits) maybe also include technical skills like experience with PyTorch etc? Those are in responsibilities but could also be skills. But we can keep Skills as the Ideal Candidate traits. - Benefits: benefits paragraph. - Pay: salary info + incentive compensation. We must not include sections with filler like "commensurate with experience". Not present. We need to preserve all substantive info. So we need to include all details from each section. Let's extract content. About the role: Paragraph 1: "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: "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." Combine into maybe two paragraphs. Responsibilities (What You’ll Do): - 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. - Collaborate with senior researchers to prepare internal tech reports or conference submissions summarizing novel methods or findings. We need to keep the dash punctuation? Use plain text. Requirements (Basic Qualifications): - Currently has, or is in the process of obtaining, a 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 or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research. We could keep as a paragraph or list. Since it's a single item, we could use
. But instruction: use
- for any list of duties/requirements/benefits. If it's a single item, still could be list. We'll use
- .
Qualifications (Preferred Qualifications): many bullet points. Need to preserve all.
Let's extract each bullet:
- 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 in deep learning theory
- Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
- Optimization (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 design
- 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
- Demonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks
- Experience designing controlled experiments and documenting reproducibility results for internal or public research
We need to keep the structure. Could flatten but preserve details. Use nested lists? The instruction only says use
- for any list. Could have nested lists. We'll do top-level ul for each major category, with li containing sub-ul if needed. Skills (The Ideal Candidate): bullet list of traits: - 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
- .
Qualifications (Preferred Qualifications): many bullet points. Need to preserve all.
Let's extract each bullet:
- 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 in deep learning theory
- Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
- Optimization (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 design
- 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
- Demonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks
- Experience designing controlled experiments and documenting reproducibility results for internal or public research
We need to keep the structure. Could flatten but preserve details. Use nested lists? The instruction only says use