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# ML Platform Engineer

- **Company:** Toyota Financial Services
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Data Validation, Data Discovery, Data Mining, Identity and Access Management, Machine Learning, Azure Machine Learning, Software Deployment, Software Engineering, Management of Software Versions, Datadog, Okta, Cloudformation, Infrastructure Automation Frameworks, Data Lineage, Machine Learning Operations, Cloudwatch, SailPoint, Terraform, Software Version Control
- **Published:** September 11, 2026
- **Apply:** https://www.themuse.com/jobs/toyotanorthamerica/senior-ml-platform-engineer

## About the Role

Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Senior ML Platform Engineer. The primary responsibility of this role is to design, build, and operationalize an enterprise-grade ML platform on AWS SageMaker Unified Studio. You will lead the organization's migration from a fragmented ML toolchain to a unified, governed environment, directly impacting how we handle the full ML lifecycle-from initial data discovery to production deployment and monitoring. Reporting to the Enterprise Platforms leadership, the person in this role will support the team's objective to scale our ML infrastructure and empower data teams to deliver high-impact AI solutions with speed and reliability., * A bachelor's degree in a relevant field that provides a strong foundation in software engineering, cloud platforms, or machine learning

 * 7+ years of software engineering experience in cloud infrastructure or ML platform operations, with experience navigating complex production environments
 * 4+ years of hands-on AWS experience, including Amazon SageMaker Studio, Pipelines, Model Registry, Endpoints, and Feature Store
 * 3+ years of experience building and operating production MLOps pipelines, including training, versioning, deployment, and rollback strategies
 * Proficiency with infrastructure-as-code tools such as Terraform, CDK, or CloudFormation to build repeatable, scalable environments
 * Deep understanding of IAM design for ML, including execution roles, service roles, and cross-account access management
 * Strong collaboration and communication skills, with the ability to work independently while partnering effectively across teams

Added bonus if you have

 * Advanced knowledge of SageMaker Unified Studio, including domain provisioning, custom blueprints, and project standardization
 * Hands-on experience with SageMaker Feature Store for online and offline feature management
 * Experience using SageMaker Model Monitor for data quality checks, bias detection, and drift detection
 * An AWS Machine Learning Specialty certification that demonstrates deeper technical expertise

## Description

In this role, you will be the architect of our ML ecosystem, ensuring that our platform is not only robust and scalable but also a seamless experience for our data scientists and engineers. Success means building a high-performance, governed environment where production workloads run reliably and innovation is accelerated through standardized, automated workflows.

 * Architect cloud-native platform capabilities that power production ML workloads and support enterprise-scale adoption
 * Drive platform standardization by standing up SageMaker Unified Studio, including domain configuration, project provisioning, and persona-based access
 * Build and maintain automated MLOps pipelines that streamline data extraction, training, model registration, and deployment
 * Govern the ML lifecycle through model versioning, lineage tracking, and cross-account promotion using SageMaker Model Registry
 * Enable reproducible experimentation by configuring MLflow for robust tracking of parameters, metrics, and artifacts
 * Strengthen platform security by implementing identity and access controls with Okta SSO and SailPoint
 * Deliver reliable real-time and batch prediction workflows while proactively monitoring model performance, drift, and data quality
 * Own platform observability and operational excellence through CloudWatch, Datadog, and root cause analysis
 * Collaborate across technical and business teams to improve workflows, remove friction, and accelerate delivery of AI solutions


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