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As a Capital One Machine Learning Engineer (MLE), you'll be part of a team focusing on observability and model governance automation. You will work with model training and features and serving metadata at scale, to enable automated model governance decisions and to build a model observability platform. You will contribute to building a system to do this for Capital One models, accelerating the move from fully trained models to deployable model artifacts ready to be used to fuel business decisioning and build an observability platform to monitor the models and platform components.
What You’ll Do
Work with model and platform teams to build systems that ingest large amounts of model and feature metadata and runtime metrics to build an observability platform and to make governance decisions.
Partner with product and design teams to build elegant and scalable solutions to speed up model governance observability
Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation big data and machine learning applications.
Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
Construct optimized data pipelines to feed machine learning models.
Use programming languages like Python, Scala, or Java
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployments of machine learning models and application code.
Basic Qualifications
Master's Degree in Computer Science or a related field
At least 15 years of experience in software engineering or solution architecture
At least 10 years of experience designing and building data intensive solutions using distributed computing
At least 10 years of experience programming with Python, Go, or Java
At least 8 years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
At least 5 years of experience productionizing, monitoring, and maintaining models
Preferred Qualifications
Master’s Degree or PhD in Computer Science, Electrical Engineering, Mathematics, or a similar field
5+ years of experience building, scaling, and optimizing ML systems
5+ years of experience with data gathering and preparation for ML models
10+ years of experience developing performant, resilient, and maintainable code.
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
5+ years of experience with distributed file systems or multi-node database paradigms.
Contributed to open source ML software
Authored/co-authored a paper on a ML technique, model, or proof of concept
5+ years of experience building production-ready data pipelines that feed ML models.
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
5+ years of experience in ML Ops either using open source tools like ML Flow or commercial tools
2+ Experience in developing applications using Generative AI i.e open source or commercial LLMs
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
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