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Salary: USD 240,000 - 295,000 / annual
Stand is building a new class of global property protection using advanced physics and AI to model catastrophic risk at the asset level. The company automates underwriting and mitigation before loss occurs, operating a scalable risk engine called the Stand World Model. The leadership team includes former founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight.
As Applied Science DevOps Lead, you will lead the platform sub-team within Applied Science, owning the technical direction, planning, and execution of critical platform initiatives. This is a player-coach role combining direct technical work with people management, project planning, and cross-team coordination. You report to the Chief Science Officer and own key projects while ensuring the broader sub-team operates effectively, grows, and delivers impact.
You will lead the platform sub-team by setting priorities, coordinating execution, and unblocking the team to deliver on critical software and infrastructure initiatives. You'll manage and grow the team through 1:1s, growth conversations, direct feedback, performance management, and mentoring as the team scales. You'll design, build, and scale core systems spanning physics simulation, AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components.
You'll own production pipelines by debugging, monitoring, and resolving issues to improve uptime and reliability. You'll build and support scalable ML infrastructure including data pipelines, training systems, evaluation frameworks, and production monitoring. You'll design and operate geospatial pipelines that merge heterogeneous spatial datasets into reproducible, production-grade workflows. You'll strengthen CI/CD and infrastructure reliability across simulation, digital-twin, and ML pipelines.
You'll drive cross-functional alignment by coordinating across Applied Science and the business, communicating modeling decisions and tradeoffs, and articulating a multi-year vision for how the team's work moves the business forward.
REQUIREMENTS:
Must-Haves:
- Technical leadership and ownership: managing and leading engineers and complex initiatives, delivering through others as well as hands-on, with strong planning, prioritization, and stakeholder coordination from concept through production.
- 5+ years building production-grade data, simulation, or modeling pipelines with end-to-end automation, monitoring, and alerting.
- Strong DevOps ownership: cloud infrastructure, infrastructure-as-code, containerization, CI/CD, automated testing, observability, and incident response for systems your team runs.
- Depth in distributed, data-heavy systems: debugging across APIs, preprocessing, modeling, and downstream consumers; cluster compute (SLURM, Kubernetes) and orchestration (Prefect, Airflow).
- Business judgment and succinct communication: translate SME needs into platform capabilities and balance research depth, timelines, and impact.
Nice-to-Haves:
- Startup or zero-to-one technology development experience.
- Hands-on data and ML infrastructure: ETL over large heterogeneous datasets, dataset construction, GPU training and inference, evaluation frameworks, and production model monitoring.
- Breadth across backend, AI pipelines, simulations, and digital twins, even if your background leans toward one area.
- Experience supporting physics-based or simulation-heavy workflows (e.g., CFD, multiphysics, digital twins) in production, with strong physical-simulation intuition.
- Knowledge of geospatial, remote-sensing, or Earth-observation datasets and systems.
- Experience working directly with business or product teams on customer-facing technology.
- Experience collaborating with MLEs on training pipelines and dataset construction for large-scale models.
