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About the Role

As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. You will be part of a team responsible for delivering hyper-personalized messages and experiences that delight customers and drive business value.

Responsibilities

Design, build, and/or deliver ML models and components that solve real-world business problems, in collaboration with Product and Data Science teams.
Inform ML infrastructure decisions using an understanding of ML modeling techniques and issues.
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 for 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, for successful deployment of ML models and application code.
Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML follows best practices in Responsible and Explainable AI.
Use programming languages like Python, Scala, or Java.

Requirements

Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering).
At least 4 years of experience programming with Python, Java, Golang, or C++.
At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn).
At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data.
At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems.
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field.
3+ years of experience optimizing ML algorithms, configurations, and infrastructure.
3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and preparing incident response plans.
3+ years of experience working with Machine Learning techniques, model types, training concepts, and model evaluation.
3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation.
Authored/co-authored a paper on a ML technique, model, or proof of concept.

Skills

Python
AWS
SQL
GenAI
PyTorch
Tensorflow
Pandas
NumPy
Scikit-learn
Spark
Ray
Kubernetes
Scala
Java
Machine Learning
Data Engineering
CI/CD
Responsible AI
Explainable AI

Location

McLean, VA
New York, NY
Plano, TX
Richmond, VA
San Francisco, CA

Work Type

Full-time

Experience Level

4 years of experience programming
4 years of Machine Learning experience
4 years of experience using and operating large scale distributed systems
2 years of experience deploying and operating Machine Learning solutions in production
3+ years of experience optimizing ML algorithms
3+ years of experience following software development best practices
3+ years of experience building resilient software solutions
3+ years of experience working with Machine Learning techniques
3+ years of experience designing, implementing, and scaling production-ready data pipelines
1+ years of experience as a technical lead

Education Level

Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field

Salary/Compensations

McLean, VA: $197,300 - $225,100
New York, NY: $215,200 - $245,600
Plano, TX: $179,400 - $204,700
Richmond, VA: $179,400 - $204,700
San Francisco, CA: $215,200 - $245,600

Benefits

Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.

About the Company

Enterprise Platforms Technology (EPTech) comprises many of Capital One’s most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices.

Equal Opportunity

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.