Center 3 (19075), United States of America, McLean, Virginia
Lead Machine Learning Engineer
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.
Team Description
Our team is on the cutting edge of GenAI and at the center of bringing our vision for AI at Capital One to life. The work of the AI Training Team touches every aspect of the model development life cycle and our deployed models in production drive business impact with visibility from our C-Suite.
- Our team creates unprecedented amounts of high quality data for training and testing GenAI models; we care about how it's created, what's in those datasets, and the impact they have.
- We are invested in building capabilities for evaluating and monitoring generative models; these methods must be state of the art, easy to use, and trusted by our users and contributors.
- Horizontal capabilities enable vertical use case work; the team builds search, summarization, RAG, and agentic workflows for integration in production applications across the company.
We learn from our colleagues, attend conferences, publish papers, and maintain strong connections to the research community. Everyone on this team has a role in realizing GenAI capabilities at Capital One, and we're excited to find experienced talent to join us.
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 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.
Basic Qualifications:
- 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
Preferred Qualifications:
- 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