Applied Machine Learning Engineer (All Levels)

Allstate•McCullom Lake, IL

About The Position

At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection. Join Allstate Technology Solutions, a pioneering force committed to revolutionizing the way our employees, agencies, and customers interact digitally. Our mission is to harness cutting-edge technology, innovative product design, and the power of artificial intelligence to create a world‑class customer experience. We aim to redefine the customer experience, ensuring consistency and operational efficiency across all touchpoints and channels. Become a part of our story. At Allstate Technology Solutions, you’ll find a collaborative and dynamic team focused on exploring new capabilities and pushing the boundaries of what’s possible. The team works in a continuous innovation cycle of ideas, research, testing, analysis, and delivery. As a Machine Learning Engineer at Allstate, you will design, build, and operate machine-learning models that deliver real business impact. You’ll work across the full ML lifecycle—including data exploration, feature engineering, model building, deployment, monitoring, and ongoing improvement. Our team emphasizes pair programming and test-driven development to ensure high-quality, reliable solutions.

Requirements

Bachelor’s degree (STEM preferred).
Entry-Level: 0–2 years (academic, internship, or professional).
Mid-Level: 3+ years building ML solutions.
Senior-Level: 3+ years deploying and operating ML systems.
Python (pandas, numpy, scikit-learn) and software engineering foundations.
Experience with ML libraries such as scikit-learn, XGBoost, LightGBM required.
SQL for data exploration and feature engineering.
Knowledge of model evaluation and interpretability (e.g., SHAP).
Willingness to learn Terraform, Java, and Typescript (no prior experience required).
Strong communication and collaboration abilities.
Ability to work with technical and non-technical partners.
Leadership and mentoring experience for senior roles.

Nice To Haves

Spark or distributed computing.
Familiarity with APIs, containers, CI/CD, monitoring, drift detection.
MLflow, SageMaker, Azure ML, Docker, CI/CD.
AWS, Azure, or GCP cloud experience.
Experience with deep learning, NLP, computer vision, or LLM/RAG.
Prior ownership of end-to-end ML products.
Insurance or financial services experience.

Responsibilities

Support model development, data exploration, testing, and deployments; collaborate through pair programming and learning best practices (Entry-Level).
Build and deploy production ML models, own key components of ML projects, and partner with cross-functional teams (Mid-Level).
Lead end-to-end ML initiatives, architect ML pipelines, mentor junior engineers, and influence technical direction (Senior-Level).