Senior Lead Machine Learning Engineer

Capital One•McLean, VA

7d•Onsite

About The Position

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 will bridge the gap between cutting-edge consumer personalization models and robust infrastructure engineering, leading your team to build intelligent, real-time digital experiences across Mobile, Web, and Email. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Within Card Tech, the Customer Intelligent Decisions & Experiences (CIDX) team is building the next generation of large-scale, Reinforcement Learning-based recommender systems that will power personalized experiences across marketing, customer servicing, and digital products for millions of Card and MainStreet customers. We directly contribute reusable capabilities to a Capital One-wide Experimentation Platform, enabling users across the enterprise to leverage machine learning for a variety of use cases.

Requirements

Bachelor’s Degree
At least 8 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 3 years of experience building, scaling, and optimizing ML systems
At least 2 years of experience leading teams developing ML solutions

Nice To Haves

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 ML models
3+ years of people management experience
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
3+ years of experience building production-ready data pipelines that feed ML models
Ability to communicate complex technical concepts clearly to a variety of audiences
Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

Responsibilities

Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, 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 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 practices in Responsible and Explainable AI
Use programming languages like Python, Scala, or Java