Software engineer jobs in Chicago, IL

About the role

We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.

What you'll bring

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 6 years of experience programming with Python, Java, Golang, or C++
• At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
• At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data
• At least 4 years of experience 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 software systems
• Master's or doctoral degree in computer science, electrical engineering, mathematics, or 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, CI/CD, etc.
• 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 preparing incident response plans
• 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, 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