Senior ML Platform Engineer - Scale AI Platforms on AWS

Toyota North America; Plano (TX); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Tuition reimbursement

Team health care plans

Relocation assistance

Job summary

Toyota North America is seeking a Senior ML Platform Engineer to design, build, and operate an enterprise-grade ML platform on AWS SageMaker Unified Studio. You will lead the migration from a fragmented toolchain to a unified, governed environment that supports the full ML lifecycle from data discovery to deployment and monitoring.

Responsibilities include building scalable MLOps pipelines, ensuring security with IAM practices, and delivering reliable real-time and batch predictions while

Qualifications

• Bachelor's degree or higher in a relevant field.
• 7+ years software engineering experience in cloud infrastructure or ML platform operations.
• 4+ years hands-on AWS experience with SageMaker Studio, Pipelines, Model Registry, Endpoints, Feature Store.
• 3+ years building and operating production MLOps pipelines (training, versioning, deployment, rollback).
• Proficiency with IaC tools (Terraform, CDK, CloudFormation).
• Strong IAM design for ML including cross-account access.

Responsibilities

• Architect and operate an enterprise-grade ML platform on AWS SageMaker Unified Studio.
• Lead migration from fragmented ML tools to a unified, governed environment.
• Build automated MLOps pipelines for data extraction, training, registration, deployment.

Skills

Software engineering

AWS cloud

ML platform ops

MLOps pipelines

Infrastructure as code

IAM security

Cross-team collaboration

Education

Bachelor's degree in CS/IS/related

Tools

AWS SageMaker Studio

SageMaker Pipelines

SageMaker Model Registry

SageMaker Endpoints

SageMaker Feature Store

Terraform/CDK/CloudFormation

Okta SSO

Job description

Toyota North America is seeking a Senior ML Platform Engineer to design, build, and operate an enterprise-grade ML platform on AWS SageMaker Unified Studio. You will lead the migration from a fragmented toolchain to a unified, governed environment that supports the full ML lifecycle from data discovery to deployment and monitoring.

Responsibilities include building scalable MLOps pipelines, ensuring security with IAM practices, and delivering reliable real-time and batch predictions while