Design, build, deploy, and run machine learning solutions at scale in a production-focused role on an Agile team.

Design, build, and/or deliver ML models and components to solve real-world business problems in partnership with Product and Data Science
Shape ML infrastructure decisions using expertise in modeling techniques and common issues, including model and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex technical problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate within a cross-functional Agile team to create and enhance software enabling big data and ML applications
Retrain, maintain, and monitor models in production
Build and/or leverage cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines that feed ML models
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful releases of ML models and application code
Ensure managed, secure code to reduce vulnerabilities, and support governance with an emphasis on risk and Responsible and Explainable AI
Use programming languages including 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
4+ years of experience programming with Python, Java, Golang, or C++
4+ years of Machine Learning experience with industry frameworks PyTorch or Tensorflow and libraries Pandas, NumPy, Scikit-learn
4+ years operating large scale distributed systems (e.g., Spark, Ray) to prepare AI or Machine Learning data
2+ years deploying and operating production ML solutions and production services in the cloud (AWS, GCP, Azure), using Kubernetes to manage large scale containerized ML systems

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

Eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI); incentives may be discretionary or non-discretionary depending on the plan
Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being

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

Machine Learning Engineer 4 (Manager, IC)

Job Description