Lead Machine Learning Engineer (Python, AWS) - New York

Salary: $215,200 - 245,600 per year Requirements:

• We require a bachelors degree.
• We need at least 6 years of experience designing and building data-intensive solutions with distributed computing; internship experience does not count.
• We need at least 4 years of programming experience with Python, Scala, or Java.
• We need at least 2 years of experience building, scaling, and optimizing machine learning systems.
• A masters or doctoral degree in computer science, electrical engineering, mathematics, or a related discipline is preferred.
• We prefer 3+ years of experience building production-ready data pipelines for machine learning models.
• We prefer 3+ years of hands-on experience with an industry-recognized machine learning framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
• We prefer 2+ years of experience developing performant, resilient, and maintainable code.
• We prefer 2+ years of experience gathering and preparing data for machine learning models.
• We prefer 2+ years of people leadership experience.
• We prefer 1+ years of experience leading teams that develop machine learning solutions using industry best practices, patterns, and automation.
• Experience developing and deploying machine learning solutions in a public cloud such as AWS, Azure, or Google Cloud Platform is preferred.
• Experience designing, implementing, and scaling complex data pipelines for machine learning models, along with performance evaluation, is preferred.
• We value machine learning industry impact through conference talks, papers, blog content, open-source contributions, or patents.
• Experience using interactive AI tools to boost productivity beyond basic code completion is preferred.

• We design, build, and deliver machine learning models and components that solve real business problems in partnership with Product and Data Science.
• We make machine learning infrastructure decisions informed by modeling methods and issues such as model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias-variance tradeoffs, and validation.
• We solve complex problems by writing and testing application code, developing and validating models, and automating testing and deployment.
• We collaborate on a cross-functional Agile team to create and enhance software for advanced big data and machine learning applications.
• We retrain, maintain, and monitor production models.
• We leverage or build cloud-based architectures, technologies, and platforms to deliver optimized machine learning at scale.
• We construct efficient data pipelines that feed machine learning models.
• We apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful model and application releases.
• We ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and our machine learning practice follows Responsible and Explainable AI standards.
• We use programming languages such as Python, Scala, or Java.

• AI
• AWS
• Azure
• Big Data
• Cloud
• Support
• Java
• Machine Learning
• Marketing
• PyTorch
• Python
• Scala
• Spark
• TensorFlow
• Model Training
• SQL

More:

We are Capital One, and our Enterprise Platforms Technology organization is hiring a Lead Machine Learning Engineer for our Marketing and Messaging team. Our team delivers hyper-personalized customer experiences and builds scalable platforms for omnichannel messaging across owned and paid adtech channels. This role sits within an Agile, cross-functional environment focused on productionizing machine learning systems at scale, with opportunities to work on cloud-based architectures, modern ML tooling, and best-in-class engineering practices. We offer a comprehensive, competitive, and inclusive benefits package that supports total well-being, along with performance-based incentive compensation. This position is associated with New York, NY compensation guidance, and we consider candidates for other approved locations based on the applicable local pay range.

last updated 32 week of 2026