Applied Science Platform Lead

About the Company

Stand is building a new class of global property protection using advanced physics and AI to model catastrophic risk at the asset level. By simulating how real-world catastrophes affect individual properties, Stand automates underwriting and mitigation to change outcomes for the insurance industry.

Responsibilities

• Lead the platform sub-team by setting priorities, coordinating execution, and unblocking technical initiatives
• Manage and grow the team through 1:1s, mentorship, and performance management
• Design, build, and scale core systems spanning physics simulation, AI, digital twins, and spatial intelligence
• Own production pipelines, including debugging, monitoring, and resolving uptime and reliability issues
• Build scalable ML infrastructure, including data pipelines, training systems, and evaluation frameworks
• Design and operate geospatial pipelines that merge heterogeneous spatial datasets into production-grade workflows
• Strengthen CI/CD and infrastructure reliability across simulation and ML pipelines
• Drive cross-functional alignment by communicating modeling decisions and articulating a multi-year technical vision

Requirements

• 5+ years building production-grade data, simulation, or modeling pipelines with end-to-end automation
• Experience leading engineers and technical initiatives through both direct contribution and delegation
• Proven ability to debug distributed, data-heavy systems
• Hands-on experience with cloud infrastructure and CI/CD (infrastructure-as-code, automated testing)
• Experience with cluster compute and job scheduling (e.g., Slurm or Kubernetes) and workflow orchestration (e.g., Prefect or Airflow)
• Ability to connect technical work to business objectives and customer impact
• Strong communication skills and judgment regarding research depth versus delivery timelines

Preferred Qualifications

• Prior people-management experience in high-growth environments
• Startup or zero-to-one technology development experience
• Experience supporting physics-based or simulation-heavy workflows (e.g., CFD, multiphysics, digital twins)
• Knowledge of geospatial, remote-sensing, or Earth-observation datasets
• Experience collaborating with MLEs on training pipelines and dataset construction