Machine Learning Engineer

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’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. 

You will be joining the team responsible for creating the Machine Learning solutions to help our customers get back on track and achieve long term success, while also meeting the financial requirements for our Card lending business. The Machine Learning solutions coming out of this team aren't just cutting edge, using advanced algorithms and reinforcement learning on a wide range of data sources, they also make up the backbone of how the Customer Resiliency business will work in the future. In other words, the models and systems we are building here are truly integral to the long term success of one of the most impactful departments in the company. Our team’s tech stack includes Docker, Python, PySpark, Kubernetes & Kubeflow. The team works closely with a team of data scientists, product owner & business partners to deliver ML solutions. If you are an experienced Software Engineer with some data engineering skillset, who is interested in starting your ML journey, you are a great candidate for this position.

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. 

Basic Qualifications:

• Bachelor’s degree 

• At least 2 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)

• At least 2 years of experience programming with Python, Scala, or Java 

• At least 1 year of Machine Learning experience with an industry recognized ML framework (scikit-learn, PyTorch, Dask, Spark, or TensorFlow) 

Preferred Qualifications:

• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform 

• 1+ years of experience working with large code bases in a team environment

• 1+ years of experience with distributed file systems or multi-node database paradigms 

• Contributed to open source ML software

• 1+ years of experience building production-ready data pipelines that feed ML models

At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 ST