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Closed on July 16, 2026.
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Machine Learning Engineer (AI Foundations)
Ai Ml
Artificial Intelligence
Cloud Operations
Data Platform
Data Science
Deep Learning
DevOps
Machine Learning
Ml Ops
TensorFlow
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Job Description
Capital One seeks a Machine Learning Engineer focused on AI Foundations to productionize ML applications at scale, collaborating with cross-functional teams on model design, deployment, monitoring, and responsible AI practices.
Responsibilities
- Design, develop, and deliver ML models and components that address real business needs, partnering with Product and Data Science teams.
- Guide ML infrastructure decisions through solid modeling knowledge, including model selection, data and feature choices, training, hyperparameter tuning, dimensionality, bias/variance management, and validation strategies.
- Tackle complex problems by writing robust application code, building and validating ML models, and automating tests and deployment processes.
- Collaborate within a cross-functional Agile team to create and enhance software supporting state of the art big data and ML applications.
- Retrain, monitor, and maintain models in production environments.
- Leverage cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
- Construct optimized data pipelines to feed ML models efficiently.
- Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
- Ensure code quality, governance, and risk management, and uphold responsible and explainable AI practices across models.
- Proficiency with programming languages such as Python, Scala, or Java.
Requirements
- Bachelor’s Degree.
- Minimum of two years of experience designing and building data-intensive solutions using distributed computing (internship experience not counted).
- Minimum of two years programming in Python, Scala, or Java.
- At least one year of machine learning experience with an industry recognized framework (scikit-learn, PyTorch, Dask, Spark, or TensorFlow).
Technologies
- Python
- Scala
- Java
- scikit-learn
- PyTorch
- Dask
- Spark
- TensorFlow
- AWS
- Azure
- Google Cloud Platform
Benefits
- Health, financial and other benefits that support total well-being.
- Performance-based incentive compensation, which may include cash bonuses and/or long term incentives (LTI).
Location
McLean, VA onsite
Compensation
USD 135,600 - 154,800 per year