Senior ML 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, TN, (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 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. What you’ll be doing 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 What you bring 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 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 Paid holidays and paid time off Referral services related to prenatal services, adoption, childcare, schools, and more Tax Advantage Accounts (Health Savings Account, H