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.
Job Description
The Allstate's Data & Analytics Technology organization is seeking a Machine Learning Platform Lead Engineer to architect, build, and scale the core platforms that power enterprise-wide machine learning solutions. In this role, you will provide deep technical leadership across ML infrastructure, MLOps automation, model deployment systems, and cloud-native engineering.
You will influence platform strategy, guide architectural decisions, and collaborate closely with data science, engineering, security, and product teams to enable reliable, scalable, and responsible ML adoption across the enterprise.
This role is ideal for a senior technologist who thrives in hands-on engineering, technical leadership, and building high-impact ML platform capabilities.
Key Responsibilities
- Serve as the technical lead for ML platform architecture, guiding system design, scalability, performance, and reliability across platform components.
- Architect and build core ML platform services, including training and compute infrastructure, feature stores, model registries, inference runtimes, and data pipelines.
- Drive architectural decisions for distributed systems, cloud‑native frameworks, and automated MLOps workflows that support enterprise-scale machine learning.
- Evaluate and integrate emerging ML platform technologies, tools, and best practices to continuously strengthen platform capabilities.
- Design and implement robust MLOps pipelines for experiment tracking, data and model versioning, CI/CD for ML, automated retraining, and model governance.
- Develop automated workflows that ensure reproducible model training, validation, deployment, and lifecycle management across multiple environments.
- Implement monitoring and observability systems for model performance, data quality, drift detection, and inference reliability.
- Build and optimize cloud-based ML infrastructure on Azure, AWS, or GCP using Kubernetes, containerization, and infrastructure‑as‑code.
- Develop scalable batch and streaming data pipelines using modern data engineering tools and frameworks.
- Embed security, compliance, responsible AI principles, and cost optimization best practices within ML platform architecture and operations.
- Collaborate with data scientists to translate modeling needs into scalable, reusable, and self-service platform capabilities.
- Work closely with security, compliance, and governance teams to ensure safe and compliant deployment of AI/ML solutions.
- Partner with application engineering teams to accelerate adoption of ML services and enable consistent, high-quality production deployments.
- Provide technical mentorship, set engineering standards, and contribute to documentation, best practices, and ongoing platform improvements.
Required Qualifications
- Extensive experience in ML engineering, platform engineering, or large-scale distributed systems.
- Deep hands-on expertise with MLOps tools, ML frameworks, model deployment techniques, and ML lifecycle automation.
- Strong proficiency in Python and backend development for machine learning systems.
- 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.
- Solid knowledge of system design, APIs, data pipelines, and scalable ML infrastructure patterns.
- Proven ability to lead technical initiatives and influence cross‑team engineering decisions.
- 6+ years of related experience (preferred).
Experience
• 6 or more years of experience (Preferred)
Supervisory Responsibilities
• This job does not have supervisory duties.
Skills
Amazon S3, Amazon SageMaker, Amazon Web Services (AWS), Azure Machine Learning Studio, Cloud Computing, Cloud Engineering, Cloud Management, Cloud Software, Cloud Technology, DevOps, Google Cloud Platform (GCP), Google Cloud Storage, Google Cloud Vertex AI Search, Lead Engineering, Machine Learning (ML), Machine Learning Methods, Machine Learning Operations, Microservice Framework, Microsoft Azure, ML Frameworks, Python (Programming Language), Python Software Development, Terraform, Terraform (Software), Vertex AI
Compensation
Base compensation offered for this role is $110,000.00 - $160,000.00 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.
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.
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