Applied Machine Learning Engineer (All Levels)

As an Applied Machine Learning Engineer within Allstate Technology Solutions, you will design, build, and operate machine learning models across the full lifecycle to deliver tangible business impact. This remote role offers a salary range of USD 110,000 to 181,025 per year.

Responsibilities

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

Requirements

• 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
• Bachelor’s degree (STEM preferred)
• Python (pandas, numpy, scikit-learn) and software engineering foundations
• ML libraries such as scikit-learn, XGBoost, LightGBM
• PyTorch or TensorFlow
• 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)

Technologies

• Python
• pandas
• numpy
• scikit-learn
• XGBoost
• LightGBM
• PyTorch
• TensorFlow
• SQL
• SHAP
• Terraform
• Java
• TypeScript
• Spark
• Docker
• MLflow
• SageMaker
• Azure ML
• AWS
• Azure
• GCP

Benefits

• Comprehensive technology setup including laptop, monitors, headset, keyboard, and mouse
• Monthly connectivity reimbursement for remote workers

Soft Skills

• Strong communication and collaboration abilities
• Ability to work with technical and non-technical partners
• Leadership and mentoring experience for senior roles

Preferred Qualifications

• 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