Capital One is looking for Machine Learning Engineers to design, build, and deliver machine learning models and supporting components that solve real business needs. In this onsite role in McLean, VA, you will help scale multi-tenant ML platforms, deploy and monitor models in production, and support end-to-end pipelines that move data from preparation to model training and serving.
You will work with Product and Data Science teams, partnering across a cross-functional Agile environment to build software for modern big data and ML applications. The position also emphasizes Responsible and Explainable AI practices, along with production reliability through CI/CD, testing, monitoring, and risk-aware governance.
Responsibilities
- Design, build, and/or deliver ML models and components for real-world business problems in collaboration with Product and Data Science teams
- Build and scale large multi-tenant platforms to support model training and/or serving at scale
- Use ML modeling knowledge to inform infrastructure decisions, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Solve complex problems through application code, testing, model development, and deployment automation
- Collaborate as part of a cross-functional Agile team to create and enhance big data and ML software
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to support successful model and application deployments
- Manage code to reduce vulnerabilities, ensure risk-aware model governance, and follow 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 (including Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 6 years programming with Python, Java, Golang, or C++
- At least 6 years ML experience with industry-standard frameworks PyTorch or Tensorflow and libraries such as Pandas, NumPy, and Scikit-learn
- At least 6 years experience using and operating large scale distributed systems such as Spark and Ray to prepare AI/ML data
- At least 4 years deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), using Kubernetes to manage large scale containerized ML systems
Technologies
- Python, Scala, Java, Golang, C++, PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
- Spark, Ray, AWS, GCP, Azure, Kubernetes
Compensation
USD 229,900 - 262,400 per year for Machine Learning Engineer 5.
Benefits
- Comprehensive, competitive, and inclusive health, financial, and other benefits that support total well-being
- Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Preferred Qualifications
- Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a 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, alarms, and incident response plans
- 5+ years of experience with ML techniques and model types such as supervised, semi-supervised, unsupervised, and reinforcement learning, including regression, classification, and clustering
- 5+ years of experience with model architectures (RNNs, CNNs, LSTMs, Transformers) and training concepts including loss function, hyperparameters, regularization, plus evaluating accuracy and diagnosing common issues like underfitting and 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
Additional information: Applications are expected to be accepted for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace.
