Lead AI Dataloop and Release Engineer

Job description

Job responsibilities

• 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 — from raw collection and labeling through curation, versioning, and delivery — ensuring the reliability and scale that training and simulation workflows demand.
• Build and maintain data flywheels that continuously improve model performance by closing the loop between deployed system behavior and future training iterations.
• Collaborate closely with teams provisioning and operating large-scale GPU/TPU training clusters to align data delivery with compute capacity and training schedules.
• Drive the design and integration of data pipelines that feed data-driven, physics-based, and high-fidelity simulators, ensuring simulated environments are realistic enough to support confident AI model validation.
• Partner with safety, validation, and certification teams to establish data quality standards and traceability practices that satisfy regulatory requirements in aviation and/or automotive domains.
• Lead, mentor, and grow a team of data and infrastructure engineers, setting technical direction and fostering a culture of rigor, ownership, and continuous improvement.
• Define and track KPIs for data pipeline health, simulation fidelity, and model readiness, using these metrics to prioritize investments and communicate progress to senior leadership.

Job requirements

• You are a software leader who thrives in enabling deployment of next-gen AI models through a comprehensive data strategy for AI model training, simulation and deployment.
• You have a technically grounded and thorough appreciation that in autonomous systems, the quality of AI model training, simulation and release infrastructure is inseparable from the performance and safety of what you…
• You've built the data flywheels and worked closely with provisioning training clusters, as well as data-driven, physics-based, and high-fidelity simulators.
• You know what it takes to engineer the data pipeline that makes simulated environments realistic enough to deploy AI models confidently in safety critical environments like aviation and automotive space.
• You are organized, methodical, and skilled at building systems that other engineers rely on every day.
• Degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, Applied Math, or a related subject.
• 5+ years of engineering experience, with at least 2 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, including dataset management, labeling workflows, and data versioning at production scale.