Senior Lead Machine Learning Engineer

capitalone; McLean (VA); On-site; USD 229,900 - 262,400; Full time

Job summary

Capital One is seeking a Senior Lead Machine Learning Engineer in McLean, VA to join the CIDX team focused on productionizing ML systems at scale. You will design, build, and deploy models, lead a cross-functional team, and guide ML infrastructure decisions across Python, Scala, or Java and cloud platforms.

You will work on real-time personalization, retrain and monitor models in production, and contribute to responsible AI practices within an Agile environment.

Qualifications

• Bachelor’s degree required; 8+ years designing data-intensive solutions with distributed computing.
• 4+ years programming in Python, Scala, or Java.
• 3+ years building, scaling, and optimizing ML systems.
• 2+ years leading teams developing ML solutions.
• Preferred: Master’s/Doctoral in CS/EE/Math or related fields; cloud experience; familiarity with major ML frameworks.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform infrastructure decisions with modeling techniques, data, and feature selection.
• Solve complex problems by writing and testing code, validating models, and automating tests/deployments.
• Collaborate in a cross-functional Agile team to create software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage cloud-based architectures to deliver ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Ensure code quality and governance with Responsible and Explainable AI practices.
• Use Python, Scala, or Java for development.

Skills

Distributed computing

Python

Scala

Java

Team leadership

ML engineering

Education

Bachelor’s Degree

Master’s or Doctoral Degree

Tools

scikit-learn

PyTorch

Spark

TensorFlow

Dask

AWS/Azure/GCP

Job description

Senior Lead Machine Learning Engineer

As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You will bridge the gap between cutting-edge consumer personalization models and robust infrastructure engineering, leading your team to build intelligent, real-time digital experiences across Mobile, Web, and Email. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

About the team: Within Card Tech, the Customer Intelligent Decisions & Experiences (CIDX) team is building the next generation of large-scale, Reinforcement Learning-based recommender systems that will power personalized experiences across marketing, customer servicing, and digital products for millions of Card and MainStreet customers. We directly contribute reusable capabilities to a Capital One-wide Experimentation Platform, enabling users across the enterprise to leverage machine learning for a variety of use cases.

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 8 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 3 years of experience building, scaling, and optimizing ML systems
• 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
• Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

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.

McLean, VA: $229,900 - $262,400 for Sr. 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.

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.