Machine Learning Engineer - Richmond

Salary: $147,100 - 201,400 per year Requirements:

• We require a bachelors degree or higher in Computer Science, Machine Learning, or a related quantitative discipline such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering.
• We require at least 3 years of programming experience with Python, Java, Golang, or C++.
• We require at least 2 years of hands-on machine learning experience using frameworks such as PyTorch or TensorFlow and libraries including Pandas, NumPy, and Scikit-learn.
• We require at least 3 years of experience working with large-scale distributed systems such as Spark or Ray to prepare AI/ML data.
• We require at least 1 year of experience deploying and operating machine learning solutions in production, including cloud environments such as AWS, GCP, or Azure, and using Kubernetes for large-scale containerized ML systems.
• We prefer a masters or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• We prefer 1+ years of experience tuning ML algorithms, configurations, and infrastructure.
• We prefer 1+ years of experience applying software development best practices such as source control, testing, code reviews, and CI/CD.
• We prefer 1+ years of experience building resilient software solutions with pre-production testing, advanced deployment methods, monitoring, alerts, and incident response planning.
• We prefer 1+ years of experience with ML techniques, model types, architectures, training concepts, and methods for evaluating accuracy and diagnosing underfitting or overfitting.
• We prefer evidence of having authored or co-authored a paper on an ML technique, model, or proof of concept.
• We prefer 1+ years of experience designing, implementing, and scaling production-ready data pipelines for ML training and evaluation.

• We design, build, and deliver ML models and components that address real business problems in partnership with Product and Data Science teams.
• We use our understanding of modeling techniques to guide ML infrastructure decisions, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
• We solve complex problems by writing and testing application code, developing and validating ML models, and automating testing and deployment.
• We collaborate on cross-functional Agile teams to create and improve software that supports advanced big data and ML applications.
• We retrain, maintain, and monitor models in production.
• We leverage or develop cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• We build optimized data pipelines to supply ML models.
• We apply continuous integration and continuous deployment best practices, including test automation and monitoring, to support reliable deployment of ML models and application code.
• We ensure code is well managed to reduce vulnerabilities, models are well governed from a risk perspective, and our ML work follows Responsible and Explainable AI best practices.
• We use programming languages such as Python, Scala, or Java.

• AI
• AWS
• Azure
• Big Data
• CI/CD
• Cloud
• GCP
• Golang
• Support
• Java
• Kubernetes
• Machine Learning
• PyTorch
• Python
• Scala
• Spark
• TensorFlow
• numpy
• pandas
• Model Training

More:

We are Capital One, and we are seeking Machine Learning Engineers to join our team and help drive major AI transformations across our business. We offer a fast-paced, collaborative, inclusive, and iterative environment where makers, breakers, doers, and disruptors work together to solve real customer problems. This role is part of a team focused on scaling production models and building cutting-edge AI and ML solutions. The position includes salary ranges that vary by location, with eligibility for performance-based incentives and a comprehensive, competitive, and inclusive benefits package supporting health, financial, and overall well-being. This role is expected to accept applications for a minimum of 5 business days.

last updated 42 week of 2026