Locations: NY - New York, United States of America, New York, New York
Lead Machine Learning Engineer
As a Capital One Machine Learning Engineer, you'll be part of an Agile team dedicated toproductionizing machine learning applications and systems at scale. You’ll participate in thedetailed technical design, development, and implementation of machine learning applicationsusing existing and emerging technology platforms. Working within an Agile environment, you’llserve as a technical lead, helping guide machine learning architectural design decisions,developing and reviewing model and application code, and ensuring high availability andperformance of our machine learning applications. You'll have the opportunity to continuouslylearn and apply the latest innovations and best practices in machine learning engineering. You’llalso mentor other engineers and further develop your technical knowledge and skills to keepCapital One at the cutting edge of technology.
We are seeking Software... Engineers who are passionate about marrying data with emerging technologies to join our team. As a Capital One Software Engineer, you’ll have the opportunity to be on the forefront of driving a major transformation within Capital One. Learn more about #lifeatcapitalone and our commitment to diversity & inclusion by jumping to slides 76-91 on our Corporate Social Responsibility Report. This requisition is an advertisement for multiple opportunities within our Tech organization. By applying to this particular role, you'll also be considered for other roles within Capital One's Engineering Organization.
What you’ll do in the role:
- Deliver ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art, next generation big data and machine learning applications.
- Leverage cloud-based architectures and technologies to deliver optimized ML models atscale.
- Construct optimized data pipelines to feed MLmodels.
- Use programming languages like Python, Scala, or Java.
- Leverage continuous integration and continuous deployment best practices, includingtest automation and monitoring, to ensure successful deployment of ML models andapplication code.
- Advocate for software and machine learning engineering best practices.
- Function as the engineering tech lead for large-scale initiatives.
- Recruit and mentor junior engineering talent.
Basic Qualifications
- Bachelor’s degree.
- At least 8 years of experience designing and building data-intensive solutions using distributed computing.
- At least 4 years of experience programming with Python, Scala, or Java.
- At least 3 years of experience building, scaling, and optimizing ML systems.
- At least 2 years of experience with the full ML development lifecycle using moderntechnology in a business critical setting.
- At least 2 years of experience leading teams developing ML solutions.
Preferred Qualifications
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
- 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
- 3+ years of experience developing performant, resilient, and maintainable code.
- 3+ years of experience with data gathering and preparation for ML models.
- 3+ years of people management experience.
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
- 3+ years of experience building production-ready data pipelines that feed ML models.
- Ability to communicate complex technical concepts clearly to a variety of audiences.
At this time, Capital One will not sponsor a new applicant for employment authorization for this position
