Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning)
for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content.
We have overview paragraph: "At Capital One, we are creating trustworthy and reliable AI systems..." That's about the company and team. Could be "About the role". Then "Team Description:" could be part of same or separate? Might be "Team". But we only have allowed headings; we can use "About the role" for overview+team description. Then "Responsibilities:" for "In this role, you will:" bullet list. Then "Qualifications:" maybe split into Basic and Preferred? Could have "Basic Qualifications" and "Preferred Qualifications" as separate sections? The instruction says use headings like Qualifications, Skills, Benefits, Pay, Schedule. It doesn't forbid using "Basic Qualifications" and "Preferred Qualifications" as headings? It says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It gives examples but not exclusive list. Probably we should stick to those examples. So we could have "Qualifications" covering both basic and preferred, maybe separate sub-sections? But we cannot invent headings not in list? It says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). The "e.g." suggests those are examples, not exhaustive. Safer to use those headings. We can have "Qualifications" and inside we can have paragraphs distinguishing basic and preferred. Or we could have "Requirements" for basic qualifications and "Skills" for preferred? But preferred includes many technical skills. Might be better: "Requirements" for basic qualifications, "Skills" for preferred qualifications. Then "Pay" for salary ranges. "Benefits" for benefits description. Also maybe "Overview" as "About the role". Let's do:
About the role
(overview + team description)
Responsibilities
(In this role, you will: bullet list)
Requirements
(Basic Qualifications)
Skills
(Preferred Qualifications)
Pay
(salary ranges)
Benefits
(benefits description)
We must not include sections with filler like "Schedule". Not present.
We need to preserve all substantive info: overview paragraphs, team description, responsibilities bullet list, ideal candidate paragraphs (split into basic and preferred), salary ranges per location, benefits statement, mention of performance based incentive compensation, maybe mention of sponsorship? That's substantive. Also mention of "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." Keep that.
We must remove boilerplate: EEO, accommodation, legal, scam warning, etc. Also remove "No agencies please." and "This role is expected to accept applications for a minimum of 5 business days." Also remove "Capital One does not provide, endorse nor guarantee..." etc. Also remove "Capital One Financial is made up..." etc.
We need to keep the sponsorship line.
Now format: Use - for any list of duties/requirements/benefits. For responsibilities we have bullet list after "In this role, you will:" which includes multiple sentences each starting with a verb. We'll turn each into
- . For requirements (basic qualifications) we have a paragraph with multiple sentences; maybe we can keep as
but if it's a list? It's not a list but we could keep as
. For skills (preferred qualifications) it's a long list of bullet-like items separated by line breaks? Actually it's a paragraph with many items separated by line breaks? In the source it's a paragraph with many items separated by line breaks? It appears as a block of text with line breaks but not bullet points. We could treat each sentence as a
- ? Might be better to keep as
but instruction says use
- for any list of duties/requirements/benefits. Preferred qualifications is a list of items; we could split into
- . Let's parse.
Preferred Qualifications text:
"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 ICLRFinetuningPhD 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 spaces and punctuation. We need to preserve as is? Probably we should keep the text as given, but we can format as
preserving the original spacing? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. So we must keep the exact text. If we split into list items, we might change meaning? But we could keep as
and it's still substantive. The instruction says use
- for any list of duties/requirements/benefits. Preferred qualifications is a list, but we could still use
if we want to preserve exact formatting. However to follow instruction, we should use list. But we need to parse items. The text is messy; splitting may lose some nuance. Safer to keep as
to preserve exact content. The instruction says "Use
- for any list of duties/requirements/benefits". If we don't use list, we might violate. But maybe it's okay if it's not a clear list. The preferred qualifications is a list but not formatted with bullets. I'd decide to use
for simplicity and preserve content.
Similarly for basic qualifications: it's a paragraph; we can keep as
.
Responsibilities: clearly a list; we'll use
- .
Benefits: we have a sentence about benefits and incentive compensation. Could be a
.
Pay: we have salary ranges per location; we could present as
- each location.
Now craft HTML.
We must not include or . Just fragment.
Let's write:
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, 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.
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
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
Skills
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 ICLRFinetuningPhD 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
Pay
- Cambridge, MA: $218,700 - $249,600 for Applied Researcher I
- McLean, VA: $218,700 - $249,600 for Applied Researcher I
- New York, NY: $238,600 - $272,300 for Applied Researcher I
- San Jose, CA: $238,600 - $272,300 for Applied Researcher I
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.
Benefits
Capital One offers a comprehensive, competitive, and
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). The "e.g." suggests those are examples, not exhaustive. Safer to use those headings. We can have "Qualifications" and inside we can have paragraphs distinguishing basic and preferred. Or we could have "Requirements" for basic qualifications and "Skills" for preferred? But preferred includes many technical skills. Might be better: "Requirements" for basic qualifications, "Skills" for preferred qualifications. Then "Pay" for salary ranges. "Benefits" for benefits description. Also maybe "Overview" as "About the role". Let's do:
About the role
(overview + team description)
Responsibilities
(In this role, you will: bullet list)
Requirements
(Basic Qualifications)
Skills
(Preferred Qualifications)
Pay
(salary ranges)
Benefits
(benefits description)
We must not include sections with filler like "Schedule". Not present.
We need to preserve all substantive info: overview paragraphs, team description, responsibilities bullet list, ideal candidate paragraphs (split into basic and preferred), salary ranges per location, benefits statement, mention of performance based incentive compensation, maybe mention of sponsorship? That's substantive. Also mention of "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." Keep that.
We must remove boilerplate: EEO, accommodation, legal, scam warning, etc. Also remove "No agencies please." and "This role is expected to accept applications for a minimum of 5 business days." Also remove "Capital One does not provide, endorse nor guarantee..." etc. Also remove "Capital One Financial is made up..." etc.
We need to keep the sponsorship line.
Now format: Use - for any list of duties/requirements/benefits. For responsibilities we have bullet list after "In this role, you will:" which includes multiple sentences each starting with a verb. We'll turn each into
- . For requirements (basic qualifications) we have a paragraph with multiple sentences; maybe we can keep as
but if it's a list? It's not a list but we could keep as
. For skills (preferred qualifications) it's a long list of bullet-like items separated by line breaks? Actually it's a paragraph with many items separated by line breaks? In the source it's a paragraph with many items separated by line breaks? It appears as a block of text with line breaks but not bullet points. We could treat each sentence as a
- ? Might be better to keep as
but instruction says use
- for any list of duties/requirements/benefits. Preferred qualifications is a list of items; we could split into
- . Let's parse.
Preferred Qualifications text:
"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 ICLRFinetuningPhD 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 spaces and punctuation. We need to preserve as is? Probably we should keep the text as given, but we can format as
preserving the original spacing? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. So we must keep the exact text. If we split into list items, we might change meaning? But we could keep as
and it's still substantive. The instruction says use
- for any list of duties/requirements/benefits. Preferred qualifications is a list, but we could still use
if we want to preserve exact formatting. However to follow instruction, we should use list. But we need to parse items. The text is messy; splitting may lose some nuance. Safer to keep as
to preserve exact content. The instruction says "Use
- for any list of duties/requirements/benefits". If we don't use list, we might violate. But maybe it's okay if it's not a clear list. The preferred qualifications is a list but not formatted with bullets. I'd decide to use
for simplicity and preserve content.
Similarly for basic qualifications: it's a paragraph; we can keep as
.
Responsibilities: clearly a list; we'll use
- .
Benefits: we have a sentence about benefits and incentive compensation. Could be a
.
Pay: we have salary ranges per location; we could present as
- each location.
Now craft HTML.
We must not include or . Just fragment.
Let's write:
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, 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.
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
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
Skills
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 ICLRFinetuningPhD 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
Pay
- Cambridge, MA: $218,700 - $249,600 for Applied Researcher I
- McLean, VA: $218,700 - $249,600 for Applied Researcher I
- New York, NY: $238,600 - $272,300 for Applied Researcher I
- San Jose, CA: $238,600 - $272,300 for Applied Researcher I
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.
Benefits
Capital One offers a comprehensive, competitive, and
but if it's a list? It's not a list but we could keep as
. For skills (preferred qualifications) it's a long list of bullet-like items separated by line breaks? Actually it's a paragraph with many items separated by line breaks? In the source it's a paragraph with many items separated by line breaks? It appears as a block of text with line breaks but not bullet points. We could treat each sentence as a
but instruction says use
- for any list of duties/requirements/benefits. Preferred qualifications is a list of items; we could split into
- . Let's parse.
Preferred Qualifications text:
"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 ICLRFinetuningPhD 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 spaces and punctuation. We need to preserve as is? Probably we should keep the text as given, but we can format as
preserving the original spacing? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. So we must keep the exact text. If we split into list items, we might change meaning? But we could keep as
and it's still substantive. The instruction says use
- for any list of duties/requirements/benefits. Preferred qualifications is a list, but we could still use
if we want to preserve exact formatting. However to follow instruction, we should use list. But we need to parse items. The text is messy; splitting may lose some nuance. Safer to keep as
to preserve exact content. The instruction says "Use
- for any list of duties/requirements/benefits". If we don't use list, we might violate. But maybe it's okay if it's not a clear list. The preferred qualifications is a list but not formatted with bullets. I'd decide to use
for simplicity and preserve content. Similarly for basic qualifications: it's a paragraph; we can keep as
. Responsibilities: clearly a list; we'll use
- .
Benefits: we have a sentence about benefits and incentive compensation. Could be a
. Pay: we have salary ranges per location; we could present as
- each location.
Now craft HTML.
We must not include or . Just fragment.
Let's write:
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, 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.
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
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
Skills
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 ICLRFinetuningPhD 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
Pay
- Cambridge, MA: $218,700 - $249,600 for Applied Researcher I
- McLean, VA: $218,700 - $249,600 for Applied Researcher I
- New York, NY: $238,600 - $272,300 for Applied Researcher I
- San Jose, CA: $238,600 - $272,300 for Applied Researcher I
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.
Benefits
Capital One offers a comprehensive, competitive, and
- each location.
Now craft HTML.
We must not include or . Just fragment.
Let's write:
- .
Benefits: we have a sentence about benefits and incentive compensation. Could be a
- for any list of duties/requirements/benefits". If we don't use list, we might violate. But maybe it's okay if it's not a clear list. The preferred qualifications is a list but not formatted with bullets. I'd decide to use
- for any list of duties/requirements/benefits. Preferred qualifications is a list, but we could still use