Distinguished Applied Researcher
Company: Capital One
Location: Newark
Posted on: October 26, 2024
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 goalsBasic 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
ResearchPreferred 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 labelingCapital One will consider sponsoring a
new qualified applicant for employment authorization for this
positionThe 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 ResearcherSan Francisco,
California (Hybrid On-site):$341,200 - $389,400 for Distinguished
Applied ResearcherCandidates 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 -. 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 . 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 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, Wayne , Distinguished Applied Researcher, Other , Newark, New Jersey
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