Machine Learning Platform Engineer

Allstate•McCullom Lake, IL

4d•$90,700 - $135,000

About The Position

The Allstate's Data & Analytics Technology organization is seeking a Machine Learning Platform Engineer to design, build, and scale the foundational platforms that power enterprise-wide machine learning development and deployment. In this role, you will work across cloud-native infrastructure, MLOps tooling, model lifecycle automation, and scalable ML systems to accelerate the adoption of AI/ML solutions across the organization. You will play a key role in shaping the core capabilities that enable data scientists and ML engineers to build reliable, secure, and production-ready models. You’ll collaborate with engineering, data science, product, and security teams to deliver high‑impact platform features while ensuring operational excellence, automation, and governance.

Requirements

Strong software engineering background with experience building distributed systems or platform services.
Hands-on experience with machine learning workflows, MLOps tooling, and productionizing ML solutions.
Proficiency in Python and familiarity with ML libraries, frameworks, and backend development patterns.
Experience with cloud platforms and ML services, including Azure ML Studio, AWS SageMaker, and/or Google Vertex AI.
Exposure to cloud storage/data such as Azure Fabric/OneLake, AWS S3, and Google Cloud Storage (GCS).
Experience with cloud-native scanning and security tools such as Azure Defender, Microsoft Purview, AWS Security Hub, Amazon Inspector, GCP Security Command Center, or equivalent services.
Strong understanding of technologies such as Kubernetes, Docker, CI/CD, Terraform/Infrastructure-as-Code, etc.
Understanding of system design, API architecture, and scalable data/ML infrastructure.
Strong communication and cross-functional collaboration skills.

Nice To Haves

4+ years of experience in ML engineering, platform engineering, or equivalent (preferred).

Responsibilities

Design, build, and operate scalable ML platform components including training infrastructure, feature stores, model registries, inference services, and end‑to-end workflow orchestration.
Develop cloud‑native, distributed systems and CI/CD pipelines that ensure reliable, reproducible, and continuously delivered ML model deployments.
Implement and mature MLOps capabilities such as experiment tracking, data and model versioning, model evaluation, monitoring, and automated retraining.
Establish best practices for model lifecycle management, testing, and deployment across development, staging, and production environments.
Integrate observability into ML systems, enabling deep visibility into performance, drift, data quality, and inference reliability.
Build and optimize cloud-based ML infrastructure on Azure, AWS, and/or GCP using Kubernetes, container orchestration, and infrastructure‑as-code tools.
Develop scalable batch and real‑time data pipelines that power feature generation, training workflows, and high‑performance model serving.
Ensure security, compliance, and cost-effectiveness across ML environments in partnership with platform, architecture, and governance teams.
Collaborate with data scientists and applied ML teams to translate modeling needs into robust, reusable, and self-service platform capabilities.
Work with security, compliance, and architecture partners to uphold responsible AI, governance, and data protection standards.
Drive developer productivity by promoting self‑service tooling, reusable components, documentation, and engineering best practices.
Contribute to Agile delivery processes while championing automation, engineering excellence, and continuous improvement.