Applied Researcher 4 (AI Foundations, VLM)
for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We must not create sections with filler like "commensurate with experience". Pay section must be included because salary info is present. Benefits section present. Schedule not present, so omit.
We need to preserve all substantive info; do not invent or summarize away detail. So we need to include all details from the posting, but we can format them.
We must not include boilerplate like EEO, accommodation, etc. Remove those.
We need to keep the salary ranges per location, mention performance based incentive compensation, benefits statement.
We need to keep the "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." That's substantive; maybe include in About the role or a separate note.
We need to keep the "This role is expected to accept applications for a minimum of 5 business days. No agencies please." That's maybe boilerplate but it's about application instructions; we should remove application instructions per instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." So remove that line.
Also remove EEO lines, accommodation, etc.
Now craft HTML.
Structure:
About the role
Intro paragraph(s) about Capital One AI, team description, etc.
Maybe combine intro and team description.
Then Responsibilities
- Partner with a cross-functional team ...
- 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
Then Skills
maybe for Ideal Candidate traits.
But we could also have Requirements
for Basic Qualifications, and Preferred Qualifications
separate. However instruction says prefer few substantive sections over many thin ones. Could combine Requirements and Qualifications? But they are distinct. Could have Qualifications
containing both basic and preferred as sublists? Might be okay.
But we need to use - for any list of duties/requirements/benefits. So we can have multiple lists.
We need to decide sections: About the role, Responsibilities, Qualifications, Pay, Benefits.
Skills maybe part of Qualities.
Let's create:
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. 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. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Then Responsibilities as above.
Then Qualifications: we can split into Basic and Preferred maybe as two subheadings? But we cannot use headings other than h3? The instruction says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but we should stick to those. Could have Basic Qualifications
and Preferred Qualifications
as they are subsections of Qualifications? Might be okay but they are not in the list. Safer to have one Qualifications
and inside we can have paragraphs describing basic and preferred, maybe using - for each bullet.
We need to preserve all details: Basic Qualifications list: 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
Preferred Qualifications: many bullet items grouped by categories: LLM, Optimization, Finetuning, Data Preparation etc. We need to preserve all.
We'll create a
- for each bullet item, preserving the text exactly as in posting.
We need to be careful not to lose any detail.
Let's extract Basic Qualifications text:
"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"
That's one bullet.
Preferred Qualifications: It's a long list with categories. We'll preserve each bullet as separate
- . The categories headings like "LLM", "Optimization (Training & Inference)", "Finetuning", "Data Preparation" are part of the bullet list? Actually they appear as headings within the Preferred Qualifications section. We need to preserve them as part of the content. Could treat each category heading as a
- maybe? But they are not bullet items; they are section headings within Preferred Qualifications. However we are only allowed to use
for section headings (like About the role etc). Using headings inside Qualifications might be okay but not specified. Safer to keep them as plain text within
- or as
. But we need to preserve all substantive information; we can include them as or just plain text.
Simplify: We'll create a - for each bullet item under Preferred Qualifications, and we will include the category headings as part of the preceding bullet? Actually the text shows:
"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 labelingDemonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworksExperience designing controlled experiments and documenting reproducibility results for internal or public research"
It seems missing spaces and punctuation. We need to preserve as is? The original text had no spaces between categories. We should keep exactly as given? Probably we should keep the text as is, but it's hard to read. However we must not invent or summarize away detail. We can keep the exact string.
Thus we can put the entire Preferred Qualifications block as a
preserving line breaks? But we need to use
- for any list of duties/requirements/benefits. The Preferred Qualifications is a list of items, but the formatting is messed. Safer to treat it as a
and preserve the text exactly.
But we risk missing the structure. However instruction: "Use
- for any list of duties/requirements/benefits". The Preferred Qualifications is a list of items, so we should use
- . But the text is not clearly delimited. We could split by the capitalized category headings: LLM, Optimization (Training & Inference), Finetuning, Data Preparation. Each category has multiple items separated by spaces. We could create
- for each category and inside include the items as text separated by commas. But that may be altering.
Given the ambiguity, maybe we can keep Preferred Qualifications as a
and note that it's a list but not using
- might violate instruction. However instruction says "Use
- for any list of duties/requirements/benefits". If we don't, we might be non-compliant. Safer to attempt to parse.
Let's try to extract items:
After "Preferred Qualifications:" we have:
"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
for prose. We must not create sections with filler like "commensurate with experience". Pay section must be included because salary info is present. Benefits section present. Schedule not present, so omit. We need to preserve all substantive info; do not invent or summarize away detail. So we need to include all details from the posting, but we can format them. We must not include boilerplate like EEO, accommodation, etc. Remove those. We need to keep the salary ranges per location, mention performance based incentive compensation, benefits statement. We need to keep the "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." That's substantive; maybe include in About the role or a separate note. We need to keep the "This role is expected to accept applications for a minimum of 5 business days. No agencies please." That's maybe boilerplate but it's about application instructions; we should remove application instructions per instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." So remove that line. Also remove EEO lines, accommodation, etc. Now craft HTML. Structure:
About the role
Intro paragraph(s) about Capital One AI, team description, etc.
Maybe combine intro and team description. ThenResponsibilities
- Partner with a cross-functional team ...
- 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
Skills
maybe for Ideal Candidate traits. But we could also haveRequirements
for Basic Qualifications, andPreferred Qualifications
separate. However instruction says prefer few substantive sections over many thin ones. Could combine Requirements and Qualifications? But they are distinct. Could haveQualifications
containing both basic and preferred as sublists? Might be okay. But we need to use- for any list of duties/requirements/benefits. So we can have multiple lists.
We need to decide sections: About the role, Responsibilities, Qualifications, Pay, Benefits.
Skills maybe part of Qualities.
Let's create:
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. 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. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Then Responsibilities as above. Then Qualifications: we can split into Basic and Preferred maybe as two subheadings? But we cannot use headings other than h3? The instruction says usefor section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but we should stick to those. Could have
Basic Qualifications
andPreferred Qualifications
as they are subsections of Qualifications? Might be okay but they are not in the list. Safer to have oneQualifications
and inside we can have paragraphs describing basic and preferred, maybe using- for each bullet.
We need to preserve all details: Basic Qualifications list: 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
Preferred Qualifications: many bullet items grouped by categories: LLM, Optimization, Finetuning, Data Preparation etc. We need to preserve all.
We'll create a
- for each bullet item, preserving the text exactly as in posting. We need to be careful not to lose any detail. Let's extract Basic Qualifications text: "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" That's one bullet. Preferred Qualifications: It's a long list with categories. We'll preserve each bullet as separate
- . The categories headings like "LLM", "Optimization (Training & Inference)", "Finetuning", "Data Preparation" are part of the bullet list? Actually they appear as headings within the Preferred Qualifications section. We need to preserve them as part of the content. Could treat each category heading as a
- maybe? But they are not bullet items; they are section headings within Preferred Qualifications. However we are only allowed to use
for section headings (like About the role etc). Using headings inside Qualifications might be okay but not specified. Safer to keep them as plain text within
- or as
. But we need to preserve all substantive information; we can include them as or just plain text. Simplify: We'll create a
- for each bullet item under Preferred Qualifications, and we will include the category headings as part of the preceding bullet? Actually the text shows:
"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 labelingDemonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworksExperience designing controlled experiments and documenting reproducibility results for internal or public research"
It seems missing spaces and punctuation. We need to preserve as is? The original text had no spaces between categories. We should keep exactly as given? Probably we should keep the text as is, but it's hard to read. However we must not invent or summarize away detail. We can keep the exact string.
Thus we can put the entire Preferred Qualifications block as a
preserving line breaks? But we need to use
- for any list of duties/requirements/benefits. The Preferred Qualifications is a list of items, but the formatting is messed. Safer to treat it as a
and preserve the text exactly. But we risk missing the structure. However instruction: "Use
- for any list of duties/requirements/benefits". The Preferred Qualifications is a list of items, so we should use
- . But the text is not clearly delimited. We could split by the capitalized category headings: LLM, Optimization (Training & Inference), Finetuning, Data Preparation. Each category has multiple items separated by spaces. We could create
- for each category and inside include the items as text separated by commas. But that may be altering.
Given the ambiguity, maybe we can keep Preferred Qualifications as a
and note that it's a list but not using
- might violate instruction. However instruction says "Use
- for any list of duties/requirements/benefits". If we don't, we might be non-compliant. Safer to attempt to parse. Let's try to extract items: After "Preferred Qualifications:" we have: "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
- might violate instruction. However instruction says "Use
- for any list of duties/requirements/benefits". The Preferred Qualifications is a list of items, so we should use
- for any list of duties/requirements/benefits. The Preferred Qualifications is a list of items, but the formatting is messed. Safer to treat it as a
- for each bullet item under Preferred Qualifications, and we will include the category headings as part of the preceding bullet? Actually the text shows:
"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 labelingDemonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworksExperience designing controlled experiments and documenting reproducibility results for internal or public research"
It seems missing spaces and punctuation. We need to preserve as is? The original text had no spaces between categories. We should keep exactly as given? Probably we should keep the text as is, but it's hard to read. However we must not invent or summarize away detail. We can keep the exact string.
Thus we can put the entire Preferred Qualifications block as a
- for each bullet.
We need to preserve all details: Basic Qualifications list: 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
Preferred Qualifications: many bullet items grouped by categories: LLM, Optimization, Finetuning, Data Preparation etc. We need to preserve all.
We'll create a