Lead Machine Learning Engineer

About the role

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 detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. The role focuses on machine‑learning architectural design, developing and reviewing model and application code, and ensuring high availability and performance of ML applications while continuously learning and applying the latest innovations and best practices.

Responsibilities

• Design, build, and deliver ML models and components that solve real‑world business problems in collaboration with Product and Data Science teams.
• Inform ML infrastructure decisions using knowledge of modeling techniques, feature selection, training, hyper‑parameter tuning, dimensionality, bias/variance, and validation.
• Write and test application code, develop and validate ML models, and automate 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 platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure code is well‑managed to reduce vulnerabilities, models are governed from a risk perspective, and ML follows Responsible and Explainable AI best practices.
• Use programming languages such as 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 related 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+ year of experience leading teams developing ML solutions using industry best practices, patterns, and automation.
• Experience developing and deploying ML solutions in a public cloud (AWS, Azure, or Google Cloud Platform).
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance.
• Demonstrated ML industry impact through conference presentations, papers, blog posts, open‑source contributions, or patents.
• Experience leveraging interactive AI tooling to accelerate productivity beyond basic code completion.

Pay

Plano, TX: $179,400 – $204,700 for Lead Machine Learning Engineer.
McLean, VA: $197,300 – $225,100 for Lead Machine Learning Engineer.
Candidates hired to work in other locations will receive the pay range associated with that location. This role is also eligible for performance‑based incentive compensation, which may include cash bonuses and/or long‑term incentives.

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support total well‑being. Eligibility varies based on full‑ or part‑time status, exempt or non‑exempt status, and management level.

Capital One will not sponsor a new applicant for employment authorization or provide immigration‑related support for this position.