Build and deploy proprietary risk management solutions using state-of-the-art AI in McLean, VA.

Design, build, and deliver machine learning models and components to solve real-world business problems with Product and Data Science teams
Build and scale massive multi-tenant platforms for large footprint ML training and/or serving
Shape ML infrastructure decisions using expertise across model choice, data and feature selection, 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 on a cross-functional Agile team to create and enhance software for big data and ML applications
Retrain, maintain, and monitor models in production
Use or build cloud-based architectures and platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Apply CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code
Ensure code is well-managed to reduce vulnerabilities, models are governed from a risk perspective, and ML follows best practices in Responsible and Explainable AI
Use programming languages such as Python, Scala, or Java

Bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of machine learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
At least 6 years operating large scale distributed systems (Spark, Ray) to prepare AI/ML data
At least 4 years deploying and operating ML solutions in production, including cloud services (AWS, GCP, Azure) and using Kubernetes to manage containerized ML systems

Master’s or doctoral degree in computer science, electrical engineering, mathematics, or related field
5+ years of experience optimizing ML algorithms, configurations, and infrastructure
5+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring and alarms, and preparing incident response plans
5+ years of experience 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 evaluating model accuracy while diagnosing common issues (underfitting, overfitting)
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences

Eligible to earn performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI)
Incentives could be discretionary or non-discretionary depending on the plan

Equal opportunity employer (EOE, including disability/veter)
Committed to non-discrimination in compliance with applicable federal, state, and local laws
Promotes a drug-free workplace
Considers qualified applicants with a criminal history in accordance with applicable laws

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position

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

Machine Learning Engineer 5 (Senior Manager, IC)

Job Description