Senior Machine Learning Engineer (Intelligent Foundations & Experiences)

Capital One; Richmond (VA); On-site; USD 120,000 - 150,000; Full time

Job summary

A financial technology company in Richmond, Virginia, is seeking a Senior Machine Learning Engineer to join their Agile team. This role involves designing and implementing machine learning applications at scale, focusing on architectural design, code development, and ensuring performance. Ideal candidates have strong programming skills and experience with machine learning frameworks. The position emphasizes collaboration and innovation in ML systems, offering a dynamic work environment and opportunities for learning.

Qualifications

• At least 4 years of experience programming with Python, Scala, or Java.
• At least 3 years designing and building data-intensive solutions using distributed computing.
• At least 1 year of experience in productionizing ML models.

Responsibilities

• Design, build, and/or deliver ML models for real-world business problems.
• Collaborate with Product and Data Science teams to enhance software.
• Ensure all code is well-managed and follows best practices in AI.

Skills

Programming with Python

Distributed computing

Machine Learning frameworks

Data pipelines

Education

Bachelor's degree

Tools

Python

Scala

Java

TensorFlow

scikit-learn

Dask

Spark

Job description

Senior Machine Learning Engineer (Intelligent Foundations & Experiences)

Join to apply for the Senior Machine Learning Engineer (Intelligent Foundations & Experiences) role at Capital One

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.

Intelligent Foundations & Experiences (IFX) is a powerful collective of horizontal technology organizations that are driving Capital One’s real-time intelligent future. Together with our partners in the Enterprise and across lines of business, we deliver broad-reaching technical solutions and advance state-of-the-art science to help every Capital One associate and our 100+M customers succeed.

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 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, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require employer support).

Capital One is an equal‑opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries.