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.

Capital One is open to hiring a Remote Employee for this opportunity.

Basic Qualifications:

Bachelor’s degree.

At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)

At least 3 years of experience designing and building data-intensive solutions using distributed computing

At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)

At least 1 year of experience productionizing, monitoring, and maintaining models

Preferred Qualifications:

1+ years of experience building, scaling, and optimizing ML systems

1+ years of experience with data gathering and preparation for ML models

2+ years of experience developing performant, resilient, and maintainable code

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

Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field

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

Contributed to open source ML software

Authored/co-authored a paper on a ML technique, model, or proof of concept

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

Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

At this time, Capital One will not sponsor a new 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.

Location is New York City: $156,596 - $184,748 for Senior Machine Learning Engineer

Location is San Francisco, California: $165,896 - $195,720 for Senior Machine Learning Engineer

Remote roles in other areas of New York & California, and across Colorado & Washington: $132,699 - $156,555 for Senior Machine Learning Engineer

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.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.