Define and execute a comprehensive data strategy that spans AI model training, simulation, and production deployment across safety‑critical autonomous systems
Own the end‑to‑end data pipeline ensuring the reliability and scale that training and simulation workflows demand
Build and maintain data flywheels that continuously improve model performance
Collaborate closely with teams provisioning and operating large‑scale GPU/TPU training clusters
Drive the design and integration of data pipelines for simulations
Partner with safety, validation, and certification teams for data quality standards
Lead, mentor, and grow a team of data and infrastructure engineers
Define and track KPIs for data pipeline health, simulation fidelity, and model readiness
Requirements
10+ years of engineering experience
at least 4 years in a technical leadership role owning data infrastructure, MLOps, or AI platform engineering at scale
demonstrated experience building and operating data pipelines for AI/ML model training
hands‑on experience integrating data systems with large‑scale distributed training infrastructure (e.g., GPU/TPU clusters)
deep understanding of simulation pipelines
experience working in or alongside safety‑critical domains
strong systems‑thinking mindset
track record of building platforms and tooling that other engineering teams depend on day‑to‑day