Machine Learning Engineer 4

Capital One’s Enterprise Platforms Technology team is hiring a Machine Learning Engineer 4 in Plano, TX to build and scale production AI and ML capabilities.

Responsibilities

• Design, build, and deliver ML models and components to address real-world business needs in partnership with Product and Data Science teams
• Guide ML infrastructure decisions using expertise in model selection and data/feature strategy, including model training, hyperparameter tuning, dimensionality considerations, bias/variance tradeoffs, and validation
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Collaborate in a cross-functional Agile environment to create and improve software for big data and ML applications
• Retrain, maintain, and monitor models operating in production
• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
• Build optimized data pipelines that support ML model training and usage
• Apply CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code
• Manage code with a focus on reducing vulnerabilities, ensure risk-governed model practices, and apply Responsible and Explainable AI best practices
• Use programming languages such as Python, Scala, or Java

Requirements

• Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 4 years programming with Python, Java, Golang, or C++
• At least 4 years ML experience with industry-standard frameworks PyTorch or Tensorflow, and libraries including Pandas, NumPy, Scikit-learn
• At least 4 years experience operating large-scale distributed systems (Spark, Ray) to prepare AI/ML data
• At least 2 years deploying and operating ML solutions in production, including operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes for large-scale containerized ML systems

Technologies

• Python, Scala, Java, Golang, C++, PyTorch, Tensorflow
• Pandas, NumPy, Scikit-learn
• Spark, Ray
• AWS, GCP, Azure
• Kubernetes, CI/CD, Agile

Education

• Bachelor’s Degree or higher in Computer Science, Machine Learning, or related quantitative field

Benefits

• Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being
• Performance based incentive compensation, which may include cash bonuses and/or long term incentives (LTI)

Enterprise Platforms Technology (EPTech)

• EPTech comprises many of Capital One’s most important enterprise platforms
• Team role includes establishing practices for building technology solutions across the company
• Team delivers capabilities that exemplify those practices

Team

• The Marketing and Messaging team delivers hyper-personalized messages and experiences for customers and prospects
• Builds scalable platforms delivering omnichannel messages in owned and paid Adtech channels

Preferred Qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
• 3+ years optimizing ML algorithms, configurations, and infrastructure
• 3+ years following software development best practices including source control, testing, code reviews, and CI/CD
• 3+ years building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation
• 3+ years working with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.), model types (Regression, Classification, Clustering, etc.), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and diagnosing and addressing common issues such as underfitting and overfitting
• 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
• 1+ years as a technical lead developing ML solutions using industry best practices, patterns, and automation
• Authored or co-authored a paper on a ML technique, model, or proof of concept

Salary

• Plano, TX (onsite): USD 179,400 - 204,700 per year
• McLean, VA: USD 197,300 - 225,100 per year
• Richmond, VA: USD 179,400 - 204,700 per year
• New York, NY: USD 215,200 - 245,600 per year
• San Francisco, CA: USD 215,200 - 245,600 per year

Work Authorization

• Capital One will not sponsor a new applicant for employment authorization or provide immigration related support for this position

Accommodation & Contact

• Accommodation requests: Capital One Recruiting at 1-800-304-9102 or [email protected]
• Technical support or recruiting process questions: [email protected]