Machine Learning Engineer 4 (Manager, IC)

Capital One is hiring a Machine Learning Engineer 4 (Manager, IC) in Chicago, IL for an onsite role. In this position, you will help design, build, deploy, and continuously monitor machine learning models and supporting components in production environments at scale, collaborating closely with Product and Data Science teams.

You will contribute across the end-to-end lifecycle, from model development and training decisions to data pipelines, release practices, and ongoing governance. The work emphasizes cloud-based architectures, CI/CD discipline, and Responsible and Explainable AI practices.

What you’ll do

• Design, build, and/or deliver machine learning models and components that address real business needs in collaboration with Product and Data Science teams
• Shape ML infrastructure decisions using expertise in modeling tradeoffs such as model selection, 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
• Work within a cross-functional Agile team to create and improve software enabling large-scale big data and machine learning applications
• Retrain, maintain, and monitor models once they are in production
• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to supply machine learning models
• Use continuous integration and continuous deployment best practices, including test automation and monitoring, to support successful releases for ML models and application code
• Manage code to reduce vulnerabilities, help ensure models are governed from a risk perspective, and apply Responsible and Explainable AI best practices
• Use programming languages including Python, Scala, or Java

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 such as Pandas, NumPy, and Scikit-learn
• At least 4 years using and operating large-scale distributed systems (such as Spark and Ray) to prepare AI or machine learning data
• At least 2 years deploying and operating machine learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and 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

Benefits

• Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
• Comprehensive, competitive, and inclusive set of health, financial, and other benefits

Preferred qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or 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 preparing incident response plans
• 3+ years of experience with machine learning techniques and model types (including supervised, semi-supervised, unsupervised, reinforcement learning; regression, classification, clustering) and model architectures (RNNs, CNNs, LSTMs, Transformers)
• 3+ years of experience with training concepts (loss function, hyperparameters, regularization) and evaluating model accuracy and 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/co-authored a paper on a ML technique, model, or proof of concept

Salary

• Chicago, IL: $179,400 - $204,700 per year
• New York, NY: $215,200 - $245,600 per year