Distinguished Applied Researcher
Company: Capital One
Location: Mount Rainier
Posted on: April 24, 2024
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Job Description:
Center 1 (19052), United States of America, McLean,
VirginiaDistinguished Applied ResearcherOverview: 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. This is an individual contributor (IC) role driving
strategic direction through collaboration with Applied Science,
Engineering and Product leaders across Capital One. As a
well-respected IC leader, you will guide and mentor a team of
applied scientists and their managers without being a direct people
leader. You will be expected to be an external leader representing
Capital One in the research community, collaborating with prominent
faculty members in the relevant AI research community. In this
role, you will: 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. 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 customers. 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. Key
Responsibilities: Partner with a cross-functional team of
scientists, machine learning engineers, software engineers, and
product managers to deliver AI-powered platforms and solutions that
change how customers interact with their money. 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 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
Flex your interpersonal skills to translate the complexity of your
work into tangible business goals Basic Qualifications: Ph.D. plus
at least 4 years of experience in Applied Research or M.S. plus at
least 6 years of experience in Applied Research Preferred
Qualifications: PhD in Computer Science, Machine Learning, Computer
Engineering, Applied Mathematics, Electrical Engineering or related
fields LLM PhD focus on NLP or Masters with 10 years of industrial
NLP research experience Core contributor to team that has trained a
large language model from scratch (10B + parameters, 500B+ tokens)
Numerous publications at ACL, NAACL and EMNLP, Neurips, ICML or
ICLR 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) Has worked on an LLM (open source or
commercial) that is currently available for use Demonstrated
ability to guide the technical direction of a large-scale model
training team Experience working with 500+ node clusters of GPUs
Has worked on LLM scaled to 70B parameters and 1T+ tokens
Experience with common training optimization frameworks (deep
speed, nemo) Behavioral Models PhD focus on topics in geometric
deep learning (Graph Neural Networks, Sequential Models,
Multivariate Time Series) Member of technical leadership for model
deployment for a very large user behavior model 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
datasets with 100m+ users Optimization (Training & Inference) PhD
focused on topics related to optimizing training of very large
language models 5+ years of experience and/or publications on one
of the following topics: Model Sparsification, Quantization,
Training Parallelism/Partitioning Design, Gradient Checkpointing,
Model Compression 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 Numerous Publications
studying tokenization, data quality, dataset curation, or labeling
Leading contributions to one or more large open source corpus (1
Trillion + tokens) Core contributor to open source libraries for
data quality, dataset curation, or labeling Capital One will
consider sponsoring a new qualified applicant for employment
authorization for this position 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. New York City (Hybrid
On-Site): $322,000 - $367,500 for Distinguished Applied Researcher
San Francisco, California (Hybrid On-site): $341,200 - $389,400 for
Distinguished Applied Researcher 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. 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. This role is expected to
accept applications for a minimum of 5 business days.No agencies
please. Capital One is an equal opportunity employer committed to
diversity and inclusion in the workplace. All qualified applicants
will receive consideration for employment without regard to sex
(including pregnancy, childbirth or related medical conditions),
race, color, age, national origin, religion, disability, genetic
information, marital status, sexual orientation, gender identity,
gender reassignment, citizenship, immigration status, protected
veteran status, or any other basis prohibited under applicable
federal, state or local law. Capital One promotes a drug-free
workplace. Capital One will consider for employment qualified
applicants with a criminal history in a manner consistent with the
requirements of applicable laws regarding criminal background
inquiries, including, to the extent applicable, Article 23-A of the
New York Correction Law; San Francisco, California Police Code
Article 49, Sections 4901-4920; New York City's Fair Chance Act;
Philadelphia's Fair Criminal Records Screening Act; and other
applicable federal, state, and local laws and regulations regarding
criminal background inquiries.If you have visited our website in
search of information on employment opportunities or to apply for a
position, and you require an accommodation, please contact Capital
One Recruiting at 1-800-304-9102 or via email at
RecruitingAccommodation@capitalone.com. All information you provide
will be kept confidential and will be used only to the extent
required to provide needed reasonable accommodations. For technical
support or questions about Capital One's recruiting process, please
send an email to Careers@capitalone.com Capital One does not
provide, endorse nor guarantee and is not liable for third-party
products, services, educational tools or other information
available through this site. Capital One Financial is made up of
several different entities. Please note that any position posted in
Canada is for Capital One Canada, any position posted in the United
Kingdom is for Capital One Europe and any position posted in the
Philippines is for Capital One Philippines Service Corp.
(COPSSC).
Keywords: Capital One, Washington DC , Distinguished Applied Researcher, Other , Mount Rainier, DC
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