Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)
AI summary of the role
Lead ML engineer on Capital One's Enterprise Platforms Technology team, responsible for productionizing ML models and systems at scale.
What you’ll do
- Design, build, and deliver ML models and components that solve real-world business problems in collaboration with Product and Data Science teams.
- Inform ML infrastructure decisions using understanding of modeling techniques including model selection, hyperparameter tuning, bias/variance, and validation.
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
- Retrain, maintain, and monitor models in production.
What you’ll bring
- At least 8 years of experience designing and building data-intensive solutions using distributed computing.
- At least 4 years of experience programming with Python, Scala, or Java.
- At least 3 years of experience building, scaling, and optimizing ML systems.
- At least 2 years of experience leading teams developing ML solutions.
Technologies
Python · Scala · Java · scikit-learn · PyTorch · Dask · Spark · TensorFlow · AWS · Azure · Google Cloud Platform
About Capital One
US consumer bank and largest credit card issuer; cloud-native operator running credit cards, auto loans, retail banking, and now the Discover network on AWS.
Public · 5000+ people
Source and classification
Internal deployment & tooling · Evidence for this classification:
Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Enterprise Platforms Technology (EPTech) comprises many of Capital One’s most important enterprise platforms. We play an essential role in establishing practices for building
More from the job description
Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. 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. What you’ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Scie [... source excerpt omitted ...] orate as part of 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/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, to ensure successful deployment of ML models and application code. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practic [... source excerpt omitted ...] ython, Scala, or Java At least 3 years of experience building, scaling, and optimizing ML systems At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 3+ years of experience developing performant, resilient, and maintainable code 3+ years of experience with data gathering and preparation for M
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