Machine Learning Engineer 4 - Machine Learning Experiences (MLX)

Capital One is seeking a Machine Learning Engineer 4 to help build and deploy machine learning models and reusable components at scale. This onsite role in McLean, VA focuses on end-to-end machine learning engineering, from modeling and data pipelines through infrastructure, deployment, and Responsible AI governance.

What you’ll do

• Design, build, and/or deliver machine learning models and components to solve real business problems in collaboration with Product and Data Science teams
• Shape ML infrastructure decisions using expertise across model and data considerations, including feature selection, training, hyperparameter tuning, dimensionality, bias/variance tradeoffs, and validation approach
• Address complex challenges by writing and testing application code, developing and validating ML models, and automating tests and deployments
• Work in a cross-functional Agile team to create and improve software supporting big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage and/or build cloud-based architectures and platforms to deliver optimized ML models at scale
• Create optimized data pipelines that supply 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 code is well-managed to reduce vulnerabilities, support risk-governed model practices, and follow best practices for Responsible and Explainable AI
• Use programming languages such as Python, Scala, or Java

Required qualifications

• 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 of experience programming with Python, Java, Golang, or C++
• At least 4 years of machine learning experience with industry-standard frameworks PyTorch or Tensorflow, and libraries including Pandas, NumPy, and Scikit-learn
• At least 4 years of experience using and operating large-scale distributed systems (Spark, Ray) to prepare AI/ML data
• At least 2 years of experience deploying and operating machine learning solutions in production, including operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large-scale containerized ML systems

Technology stack

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

Preferred qualifications

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

Location and salary

This onsite role is located in McLean, VA. The annual salary range is USD 197,300 - 225,100. The position may also be eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI).

Additional information

• Capital One expects to accept applications for a minimum of 5 business days
• No agencies please
• Capital One is an equal opportunity employer (EOE), committed to non-discrimination in compliance with applicable laws
• Capital One promotes a drug-free workplace
• Capital One will consider applicants with a criminal history in accordance with applicable laws
• Capital One does not sponsor new applicants for employment authorization or offer immigration-related support for this position