Platform Engineer

• Build and operate scalable AI data, training, evaluation, and MLOps infrastructure
• Support perception, prediction, VLA, and scheduling-optimization teams in developing, validating, and managing AI models
• Build data collection, ingestion, processing, and storage capabilities for autonomous vehicle and mining-site data
• Establish dataset versioning, metadata, annotation workflows, and data-quality mechanisms
• Manage sensor data, vehicle status, actions, operational data, simulation data, Corner Cases, Failure Cases, Demonstration Data, and multimodal Vision-Language-Action data
• Build reusable training jobs, compute scheduling, experiment tracking, and automated evaluation capabilities
• Support traditional ML, deep learning, VLM/VLA, and back-office optimization model development
• Build Model Registry, Artifact management, and model lifecycle processes
• Maintain traceability among code, data, configuration, checkpoints, models, and evaluation results
• Provide model packaging, candidate-version management, Deployment Pipeline Infrastructure, and rollback mechanisms
• Improve pipeline stability, scalability, resource utilization, and developer experience
• Establish monitoring, alerting, and issue-diagnosis capabilities
• Collaborate with perception, prediction, planning, VLA, scheduling optimization, AI Runtime, Validation, Cybersecurity, and Functional Safety teams
• Convert common cross-team requirements into standardized platform capabilities

Explore AI Platform Engineer at Caterpillar Inc.. Review requirements and application details.

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