Join Capital One as a Lead Machine Learning Engineer, where you'll work in an Agile team to design, develop, and optimize machine learning applications that solve real-world business challenges. This role offers the chance to leverage cutting-edge technologies and best practices in machine learning engineering while ensuring high availability and performance of applications.
Key Responsibilities
Design, build, and/or deliver ML models and components that solve real-world business problems
Inform ML infrastructure decisions using understanding of ML modeling techniques
Solve complex problems by writing and testing application code, developing and validating ML models
Collaborate as part of a cross-functional Agile team to create and enhance software for ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures for optimized ML models
Construct optimized data pipelines to feed ML models
Leverage continuous integration and deployment best practices
Ensure code management to reduce vulnerabilities and follow best practices in Responsible and Explainable AI
Use programming languages like Python, Scala, or Java
Required Qualifications
Bachelor’s degree
At least 6 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 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
Benefits & Perks
Comprehensive health benefitsFinancial benefitsPerformance-based incentive compensation including bonuses and long-term incentivesInclusive workplace environment