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.

10+ years of engineering experience, with 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, including dataset management, labeling workflows, and data versioning at production scale.

Hands-on experience integrating data systems with large-scale distributed training infrastructure (e.g., GPU/TPU clusters, job orchestration, experiment tracking).

Deep understanding of simulation pipelines — including data-driven, physics-based, or sensor-realistic simulators — and how data quality directly impacts simulator fidelity and model transferability.

Experience working in or alongside safety-critical domains (autonomous vehicles, aviation, robotics, or similar) with an understanding of what rigor, traceability, and validation mean in that context.

Strong systems-thinking mindset: you reason about data quality, pipeline reliability, and infrastructure design as interconnected constraints, not isolated problems.

Track record of building platforms and tooling that other engineering teams depend on day-to-day, with a high bar for reliability, documentation, and developer experience.

Excellent cross-functional communication skills — able to translate technical data strategy into clear priorities for product, safety, and executive stakeholders.

Experience with domain randomization, synthetic data generation, or sensor simulation techniques used to bridge the sim-to-real gap in autonomous systems.

Familiarity with aviation-specific standards (e.g., DO-178C, DO-254) or automotive safety frameworks (e.g., ISO 26262, SOTIF) as they relate to data and software validation.

Prior experience building or scaling data flywheel systems — closed-loop pipelines that feed real-world deployment signals back into training and labeling workflows.

Hands-on background with perception, planning, or control model development in autonomous vehicles or UAV/UAS systems.

Experience with formal data governance, lineage tracking, or provenance tooling in regulated environments.

Contributions to open-source tooling in the MLOps, data engineering, or simulation space.