Senior Machine Learning Platform Engineer

Overview Who we are Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world’s most admired brands, Toyota is growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We’re looking for talented team members who want to Dream. Do. Grow. with us. An important part of the Toyota family is Toyota Financial Services (TFS), the finance and insurance brand for Toyota and Lexus in North America. While TFS is a separate business entity, it is an essential part of this world-changing company- delivering on Toyota's vision to move people beyond what's possible. At TFS, you will help create best-in-class customer experience in an innovative, collaborative environment. Toyota does not offer support or sponsorship of job applicants for employment-based visas or any other work authorization for this role now or in the future. You must have the right to work in the United States and not require Toyota support or sponsorship for immigration-related employment (e.g., H-1B, O-1, E-3, H-1B1, TN, F-1 OPT, F-1 STEM OPT, F-1 CPT, ‘job flexibility benefits’ [also known as I-140 or Adjustment of Status portability], etc.) now or in the future. You should not apply for this role if you will require Toyota to assist with immigration support or sponsorship now or in the future. Who we’re looking for Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Senior Machine Learning Platform Engineer . The primary responsibility of this role is to design, build, and operationalize an enterprise-grade ML platform on AWS SageMaker AI or Unified Studio. You will contribute to the organization’s migration from a fragmented ML toolchain to a unified, governed environment, supporting how we handle the full ML lifecycle from 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. What you’ll be doing Design, build, and enhance cloud-native MLOps platform components for production ML workloads, including SageMaker domain configuration, project provisioning, and persona-based roles. Build SageMaker Pipelines for Snowflake data extraction, preprocessing, training, evaluation, and model registration; configure MLflow auto-logging for parameters, metrics, and artifacts. Manage SageMaker Model Registry, including cross-account promotion, versioning, immutability, and lineage; support real-time SageMaker endpoints and batch prediction workflows. Implement identity and access management with Okta SSO, SailPoint entitlements, persona-based execution roles, and pipeline service roles. Set up model monitoring for data drift, model drift, and performance degradation, and configure a data catalog with searchable datasets, access visibility, request workflows, and lineage. Support platform operations through CloudWatch and Datadog observability, logging, custom images, and instance availability; troubleshoot production issues, perform root cause analysis, and drive improvements. Stay current with evolving AI/ML platform technologies and engineering best practices. What you bring Bachelor’s degree in a relevant field that provides a strong foundation in software engineering, cloud platforms, or machine learning. 4+ years of DevOps or Software Engineering experience in cloud infrastructure or ML platform operations. 2+ years of hands-on AWS experience, including Amazon SageMaker Studio, Pipelines, Model Registry, Endpoints, and Feature Store. 2+ 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, and scalable environments. Working 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 AI, including domain provisioning, custom blueprints, and project standardization. An AWS Machine Learning Specialty certification that demonstrates deeper technical expertise. What we’ll bring During your interview process, our team can fill you in on all the details of our industry-leading benefits and career development opportunities. A few highlights include: A work environment built on teamwork, flexibility, and respect. Professional growth and development programs to help advance your career, as well as tuition reimbursement. Team Member Vehicle Purchase Discount. Toyota Team Member Lease Vehicle Program (if applicable). Comprehensive health care and wellness plans for your entire family. Toyota 401(k) Savings Plan featuring a company match, as well as an annual retirement contribution from Toyota regardless of whether you contribute (if applicable). Paid holidays and paid time off. Referral services related to prenatal services, adoption, childcare, schools and more. Tax Advantaged Accounts (Health Savings Account, Health Care FSA, Dependent Care FSA). Relocation assistance (if applicable). Belonging at Toyota Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Applicants for our positions are considered without regard to race, ethnicity, national origin, sex, sexual orientation, gender identity or expression, age, disability, religion, military or veteran status, or any other characteristics protected by law. Have a question, need assistance with your application or do you require any special accommodations? Please send an email to [email protected] .