As a Senior Lead Machine Learning Engineer at Capital One, you will design and implement machine learning applications scaled for production, focusing on architectural design and model validations. You’ll work in an Agile team, collaborating with cross-functional experts to deliver optimized ML systems, ensuring robust performance while adhering to best practices in Responsible and Explainable AI. Additionally, you will leverage cloud-based platforms and manage data pipelines to enhance customer experience through advanced analytics and machine learning solutions.

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’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. 

What you’ll do in the role: 

• 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.  

About the Team

In the Enterprise Data Tech Organization customer experience is at the forefront of what we do. This team builds functional, always on scalable data ecosystems working alongside some of the savviest Data techies in the industry, enabling products and solutions to enhance customer experience and drive up satisfaction levels. In addition, the team manages/builds data solutions, solving customer reported problems, identifying and solving production issues, and implementing integrated solutions that meet our customers’ needs.

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 

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.

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.

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 

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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).

Machine Learning, Artificial Intelligence, Data Engineering, Cloud Computing, Python, Agile Development, Model Validation, Big Data, Data Pipelines, Continuous Integration