Senior Machine Learning Engineer - Production ML & Scale

Capital One; Richmond (VA); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer to productionize ML applications at scale in an Agile setting. You will design, build, and deploy ML components, and collaborate with Product and Data Science teams to address real-world problems.

Expect emphasis on scalable data pipelines, cloud architecture, and responsible AI. You will work with Python/Scala/Java, contribute to ML infrastructure, and ensure models are retrained and monitored in production, with a focus on high availability

Qualifications

• Bachelor’s degree in a relevant field.
• 6+ years of experience designing and building data-intensive solutions using distributed computing.
• 4+ years of experience programming with Python, Scala, or Java.
• 2+ years of experience building, scaling, and optimizing ML systems.
• Preferred: experience with production-ready data pipelines and ML frameworks.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions, including model/data selection and tuning.
• Write and test application code, validate ML models, and automate tests and deployment.
• Collaborate in an Agile team to create software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.
• Construct data pipelines to feed ML models.
• Apply CI/CD, test automation, and monitoring for successful deployments.
• Ensure code quality, governance, and responsible AI practices.

Skills

Python/Scala/Java

Distributed computing

ML concepts

Agile / CI-CD

Cloud technologies

Data pipelines

ML frameworks

Education

Bachelor's Degree

Master's/Doctoral preferred

Tools

scikit-learn

PyTorch

TensorFlow

Spark

Dask

Job description

Capital One is seeking a Machine Learning Engineer to productionize ML applications at scale in an Agile setting. You will design, build, and deploy ML components, and collaborate with Product and Data Science teams to address real-world problems.

Expect emphasis on scalable data pipelines, cloud architecture, and responsible AI. You will work with Python/Scala/Java, contribute to ML infrastructure, and ensure models are retrained and monitored in production, with a focus on high availability