Senior Manager, Software Engineering, Machine Learning - Software; Remote

Senior Manager, Software Engineering, Machine Learning - Capital One Software (Remote)

Ever since our first credit card customer in 1994, Capital One has recognized that technology and data can enable even large companies to be innovative and personalized. As one of the first large enterprises to go all-in on the public cloud, Capital One needed to build cloud and data management tools that didn’t exist in the marketplace to enable us to operate at scale in the cloud.

And in 2022, we publicly announced Capital One Software and brought our first B2B software solution, Slingshot, to market.

Building on Capital One’s pioneering adoption of modern cloud and data capabilities, Capital One Software is helping accelerate the data management journey at scale for businesses operating in the cloud. If you think of the kind of challenges that companies face – things like data publishing, data consumption, data governance, and infrastructure management – we’ve built tools to address these various needs along the way.

Capital One Software will continue to explore where we can bring our solutions to market to help other businesses address these same needs going forward.

As a Capital One Machine Learning Engineer (MLE), you'll be part of software team dedicated to producing machine learning applications and systems ’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.

You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

• 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.

• 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
• At least 4 years of people management experience.

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• 4+ years of on‑the‑job experience with an industry recognized ML framework such as scikit‑learn, PyTorch, Dask, Spark, or Tensor Flow
• 3+ years of experience developing performant, resilient, and maintainable code
• 3+ years of experience with data gathering and preparation for ML models
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• 3+ years of experience building…