Dask Data Jobs

2 jobs found

2mo ago

Lead Machine Learning Engineer (Enterprise Platforms Technology)

Capital One McLean, VA

McLean, VA

2mo ago Closed

$197k - $225k/yr Full time AWS Azure Big Data

This position is no longer accepting applications (closed Jun 9, 2026).

Job Description

This on-site role in McLean, VA focuses on productionizing ML applications and systems at scale within Capital One's Enterprise Platforms Technology group.

Responsibilities

Design, build, and deliver ML models and components that address real world business problems, in collaboration with Product and Data Science teams.
Inform ML infrastructure decisions through understanding of modeling techniques and issues, including model selection, data, feature engineering, 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 within 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 platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure code is well governed to minimize vulnerabilities, and that models meet risk and Responsible AI / Explainable AI standards.
Use programming languages such as Python, Scala, or Java.

Requirements

Bachelor’s Degree.
At least 6 years of experience designing and building data-intensive solutions with distributed computing (internship experience does not apply).
At least 4 years of experience programming with Python, Scala, or Java.
At least 2 years of experience designing, scaling, and optimizing ML systems.

Technologies

Python, Scala, Java
scikit-learn, PyTorch, Dask, Spark, TensorFlow
AWS, Azure, Google Cloud Platform

Benefits

Health benefits
Financial benefits
Performance-based incentive compensation (cash bonuses and/or long-term incentives)

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2mo ago

Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS)

Capital One Richmond, VA

Richmond, VA

2mo ago Closed

$179k - $205k/yr Full time AWS Big Data Cloud

This position is no longer accepting applications (closed Jun 9, 2026).

Job Description

Capital One is seeking a Lead Machine Learning Engineer to scale ML solutions across the organization, focusing on architecture, model development, deployment, and operations within an Agile environment. This role centers on productionizing ML applications and systems, building robust data pipelines, and delivering ML capabilities at scale.

Location

Richmond, VA (onsite)

Compensation

Salary: USD 179,400 - 204,700 per year

Responsibilities

Design, build, and deliver ML models and components that address real-world business problems, partnering with Product and Data Science teams.
Inform ML infrastructure decisions using knowledge of modeling techniques, including model choice, data and feature selection, 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 within a cross-functional Agile team to create and improve software for state-of-the-art big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud-based architectures and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and code.
Ensure code quality and governance, manage risk for models, and adhere to Responsible and Explainable AI practices.
Use programming languages such as Python, Scala, or Java.

Requirements

Bachelor’s degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
At least 4 years of experience programming with Python, Scala, or Java
At least 2 years of experience building, scaling, and optimizing ML systems

Technologies

KServe, Kubernetes, PyTorch, TensorFlow
AWS, Python, Scala, Java
scikit-learn, Dask, Spark

Benefits

This role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI). Incentives can be discretionary or non-discretionary depending on the plan.
Health, financial, and other benefits that support total well-being.

Team Description

The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in res

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