Lead Machine Learning Engineer (Manager IC)

Capital One•San Jose, CA

5d•$197,300 - $245,600•Onsite

About The Position

As a Capital One Machine Learning Engineer (MLE), you'll join an Agile team building and productionizing foundation models at scale. Our work centers on self-supervised learning for transformer architectures — pretraining on Capital One's rich, large-scale behavioral data to learn representations that power applications across use cases such as fraud, marketing, and servicing. You'll participate in the detailed technical design, development, and implementation of these systems, spanning model architecture, large-scale training and representation learning, and the engineering required to serve models reliably in production. You'll develop and review model and application code, drive machine learning architectural decisions, and ensure the high availability and performance of our applications. And you'll have the opportunity to continuously learn and apply the latest innovations in self-supervised learning, transformer modeling, and ML engineering best practices.

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

Nice To Haves

Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
3+ years of experience building production-ready data pipelines that feed ML models
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
2+ years of experience developing performant, resilient, and maintainable code
2+ years of experience with data gathering and preparation for ML models
2+ years of people leader experience
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
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