Machine Learning Platform Engineer

About the Company

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.

About the Role

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.

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.

Qualifications

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

Required Skills

• Amazon S3
• Amazon SageMaker
• Amazon Web Services (AWS)
• Azure Machine Learning Studio
• CI/CD
• Cloud Automation
• Cloud Computing
• Cloud Deployment
• Cloud Operations
• Cloud Software
• DevOps
• Google Cloud Platform (GCP)
• Google Cloud Storage
• Google Cloud Vertex AI Search
• Kubernetes
• Machine Learning (ML)
• Machine Learning Operations
• Microservice Framework
• Microsoft Azure
• Microsoft Cloud
• Microsoft Defender
• ML Frameworks
• MLlib
• Python Automation

Preferred Skills

• 2 more skills related to the position.

Pay range and compensation package

Base compensation offered for this role is $90,700\.00 - $135,000\.00 annually and is based on experience and qualifications. Total compensation for this role is comprised of several factors, including the base compensation outlined above, plus incentive pay (i.e., commission, bonus, etc.) as applicable for the role.

Equal Opportunity Statement

The candidate(s) offered this position will be required to submit to a background investigation. Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact. Allstate generally does not sponsor individuals for employment-based visas for this position.

Effective July 1, 2014, under Indiana House Enrolled Act (HEA) 1242, it is against public policy of the State of Indiana and a discriminatory practice for an employer to discriminate against a prospective employee on the basis of status as a veteran by refusing to employ an applicant on the basis that they are a veteran of the armed forces of the United States, a member of the Indiana National Guard or a member of a reserve component.

For jobs in San Francisco, please click