Machine Learning Engineer
Recruiting: VA - McLean, United States of America, McLean, Virginia
At Capital One, we're building a leading information-based technology company. Still founder-led by Chairman and Chief Executive Officer Richard Fairbank, Capital One is on a mission to help our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We measure our efforts by the success our customers enjoy and the advocacy they exhibit. We are succeeding because they are succeeding.
Guided by our shared values, we thrive in an environment where collaboration and openness are valued. We believe that innovation is powered by perspective and that teamwork and respect for each other lead to superior results. We elevate each other and obsess about doing the right thing. Our associates serve with humility and a deep respect for their responsibility in helping our customers achieve their goals and realize their dreams. Together, we are on a quest to change banking for good.
Machine Learning Engineer
Machine Learning Engineer – C4ML
Are you a high performing software/system engineering-hybrid looking to take part in some of the most cutting edge research and production projects? Do you read constantly about advancements in various applied machine learning architectures and solution white papers? Would you like to take part or drive the creation of publishable advancements in machine learning across various disciplines? You could be a perfect match for a Machine Learning Engineer role at Capital One's Center for Machine Learning (C4ML).
As a Machine Learning Engineer in C4ML, you will contribute to building fast architectures and machine learning solutions to create or improve some of the most interesting use cases in the financial services industry. Our projects revolve around advances in system/model design and cloud based deployment for resilient production systems. Capital One maintains a full stack of technology solutions including streaming big data, state of the art machine learning, micro-service architecture, distributed computation engines, and intuitive visualizations in the cloud. To manage this, we are working with several cutting-edge technologies and are actively developing and contributing to the open source community. We are highly technical with strong backgrounds in our fields to support use cases ranging from cyber threat prevention to sophisticated NLP semantic understanding in an always on 24/7 service architecture. We have the highest executive support for acting as a catalyst of machine learning across Capital One providing our researchers extraordinary diversity in topics.
What you will bring to the role:
- Excellent communication skills evidenced by multiple white papers (internal proprietary or externally published).
- Demonstrated ability to build full stack systems architected for speed and distributed computing.
- Demonstrated ability to quickly learn new tools and paradigms to deploy cutting edge solutions.
- Experience mentoring junior engineers.
- Adept at simultaneously working on multiple projects, meeting deadlines, and managing expectations.
What you will do in the role:
- Act as an independent consultant to various lines of business to help create or improve projects.
- Develop both deployment architecture and scripts for automated system deployment in AWS.
- Code new machine learning paradigms, sometimes from first principles, for integration into production systems.
- Learn and work with subject matter experts to create large scale deployments using newly researched methodologies.
- Construct data staging layers and fast real-time systems to feed machine learning algorithms.
- Create white papers, attend conferences, and contribute to open source software.
- Bachelor's Degree or Military Experience
- At least 2 years of experience designing and building full stack solutions utilizing distributed computing.
- At least 2 years of experience integrating with larger code bases or materially important pull requests against larger projects.
- At least 2 years of experience working with Python, Scala, or Java.
- At least 2 years of experience with leading distributed file systems and multi-node database paradigms.
- Master's Degree
- At least 4 years of experience in designing and building full stack solutions utilizing distributed computing.
- At least 6 years of experience integrating with larger code bases.
- At least 4 years working with Python, Scala, and Java.
- At least 4 years of experience with leading distributed file systems and multi-node database paradigms.
- At least 2 years leading teams in code development and balancing feature requests with feasibility constraints.
- A history of publications and conference attendance.
At this time, Capital One will not sponsor a new applicant for employment authorization for this position.
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, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, 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
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