Hybrid Senior Machine Learning Engineer

at Capital One

• Job title: Senior Machine Learning Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: $158,600 - $181,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Google Cloud Platform, Java, Node.js, Open Source, Python, PyTorch, Scala, Scikit-Learn, Spark, Tensorflow
• Location requirements: Hybrid, New York City, United States

About the role

• Join Capital One as a Machine Learning Engineer, working on productionizing ML applications. Focus on designing, building, and maintaining robust ML infrastructures and solutions.

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 and issues
• 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 for ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Leverage CI/CD best practices to ensure successful deployment of ML models
• Ensure all code is well-managed to reduce vulnerabilities and governance of models for risk
• Use programming languages like Python, Scala, or Java

Requirements

• Bachelor’s Degree
• At least 4 years of experience programming with Python, Scala, or Java
• At least 3 years of experience designing and building data-intensive solutions using distributed computing
• At least 2 years of on-the-job experience with industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
• At least 1 year of experience productionizing, monitoring, and maintaining models
• 1+ years of experience building, scaling, and optimizing ML systems
• 1+ years of experience with data gathering and preparation for ML models
• 2+ years of experience developing performant, resilient, and maintainable code
• Experience developing and deploying ML solutions in a public cloud (AWS, Azure, or Google Cloud Platform)
• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• 3+ 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
• 3+ 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

Benefits

• Comprehensive health benefits
• Competitive bonuses
• Performance-based incentives