ML Platform Engineer

UniversalAGI is building foundation models that learn physics directly from data for industrial and defense applications. The ML Platform Engineer will build and own the execution platform for data generation, simulation orchestration, training and fine-tuning infrastructure, benchmarking pipelines, and production deployments, including customer cloud and on-premise environments.

Responsibilities:

• Build and operate scalable infrastructure for data generation and simulation workflows (job orchestration, scheduling, queues, retries, observability)
• Build reproducible pipelines for training/fine-tuning and benchmarking (artifact/version management, experiment tracking, dataset lineage)
• Own cost/performance tradeoffs across compute, storage, networking, and runtime efficiency
• Lead deployments of our stack into customer cloud/on-prem environments (AWS/GCP/Azure + hybrid), including secure networking, permissions, and data movement
• Build robust deployment patterns: environment provisioning, CI/CD, rollbacks, monitoring, and incident response
• Partner with customers to ensure reliability and repeatability under real-world constraints (security, compliance, infra limits, data governance)