Lead Machine Learning Engineer (Manager IC)

Overview

Lead Machine Learning Engineer (Manager IC)

As a Capital One Machine Learning Engineer (MLE), you'll join an Agile team building and productionizing foundation models at scale. Our work centers on self-supervised learning for transformer architectures — pretraining on Capital One's rich, large-scale behavioral data to learn representations that power applications across use cases such as fraud, marketing, and servicing . You'll participate in the detailed technical design, development, and implementation of these systems, spanning model architecture, large-scale training and representation learning, and the engineering required to serve models reliably in production. You'll develop and review model and application code, drive machine learning architectural decisions, and ensure the high availability and performance of our applications. And you'll have the opportunity to continuously learn and apply the latest innovations in self-supervised learning, transformer modeling, and ML engineering best practices.

What You’ll Do:

The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:

• Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams

• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation)

• 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 big data and ML applications

• Retrain, maintain, and monitor models in production

• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.

• Construct optimized data pipelines to feed ML models

• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code

• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI

• Use programming languages like Python, Scala, or Java

Basic Qualifications:

• Bachelor’s Degree 

• At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)

• At least 4 years of experience programming with Python, Scala, or Java

• At least 2 years of experience building, scaling, and optimizing ML systems

Preferred Qualifications:

• Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field

• 3+ years of experience building production-ready data pipelines that feed ML models 

• 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 

• 2+ years of experience developing performant, resilient, and maintainable code

• 2+ years of experience with data gathering and preparation for ML models

• 2+ years of people leader experience

• 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation 

• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform

• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance 

• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents 

• Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).

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.

Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer

McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer

New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer

San Jose, CA: $215,200 - $245,600 for Lead Machine Learning Engineer

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 (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. 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 Show phone number or via email at View email address on capitalonecareers.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 View email address on capitalonecareers.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).