Machine Learning Engineer 4

Capital One is hiring a Machine Learning Engineer 4 to support Enterprise Platforms Technology (EPTech) and the Marketing and Messaging team. The role focuses on designing, building, deploying, and monitoring machine learning models and pipelines at scale, with responsible AI practices and cloud-based architectures.

Role Focus

This position supports EPTech and the Marketing and Messaging organization. The Marketing and Messaging team delivers hyper-personalized messages and experiences that improve customer engagement, attract prospects, and drive increasing business value. The team also builds scalable platforms for omnichannel message delivery across owned and paid Adtech channels.

Key Responsibilities

• Design, build, and/or deliver machine learning models and components that address real-world business problems in collaboration with Product and Data Science teams.
• Apply knowledge of ML modeling techniques and challenges to inform infrastructure decisions, including model 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.
• Participate in a cross-functional Agile team to create and improve software supporting big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Use or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines that feed ML models.
• Apply CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code.
• Help ensure code is well-managed to reduce vulnerabilities, models are governed from a risk perspective, and ML work follows Responsible and Explainable AI best practices.
• 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 PyTorch or TensorFlow and libraries including Pandas, NumPy, and Scikit-learn.
• At least 4 years of experience using and operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data.
• At least 2 years of experience deploying and operating machine learning solutions in production and operating production services in cloud environments (AWS, GCP, Azure), including using Kubernetes for large-scale containerized ML systems.
• 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 following software development best practices including source control, testing, code reviews, and CI/CD.
• 3+ 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 plan preparation.
• 3+ years of experience with ML techniques including supervised, semi-supervised, unsupervised learning, and reinforcement learning, along with model types such as regression, classification, and clustering.
• 3+ years of experience with model architectures including RNNs, CNNs, LSTMs, and Transformers, plus training concepts like loss functions, hyperparameters, and regularization, and evaluating model accuracy while diagnosing 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.

Technologies

• Programming: Python, Scala, Java, Golang, C++
• Cloud and Infrastructure: AWS, GCP, Azure, Kubernetes
• Machine Learning: PyTorch, Tensorflow
• Data and Libraries: Pandas, NumPy, Scikit-learn
• Distributed Systems: Spark, Ray
• Delivery Practices: CI/CD
• Model Architectures: RNNs, CNNs, LSTMs, Transformers

Compensation and Location

Location: New York, NY (onsite).

Salary range: USD 215,200 - 245,600 per year.

Note: Candidates hired to work in other locations will be subject to the pay range associated with that location.

Benefits

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

Additional Information on Entities

• Positions posted in Canada are for Capital One Canada.
• Positions posted in the United Kingdom are for Capital One Europe.
• Positions posted in the Philippines are for Capital One Philippines Service Corp. (COPSSC).