Applied Science DevOps Lead

📋 Job Overview

At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine, our Stand World Model.

We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.

Our leadership team includes former successful founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight, bringing deep experience in building and scaling high-growth companies.

🏢 About Stand

The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable.

Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can’t and operate with far less friction.

🎯 The Role

As the Applied Science DevOps Lead, you’ll lead the platform sub-team within Applied Science that helps build, deploys, and supports Stand’s flagship physics simulations, digital twins, AI/ML models, and spatial intelligence. You’ll own the technical direction, planning, and execution of critical platform initiatives — ensuring they align with Applied Science and companywide stakeholders, ship on schedule, and deliver measurable outcomes. The role spans production AI training and inference, simulation pipelines, infrastructure and CI/CD reliability, geospatial data processing, annotation and QC systems, and new digital-twin capabilities that support Stand’s expansion into new geographies and perils (e.g., hurricane).

This is a player-coach role, combining direct technical work with the leadership around it: people management, project planning, cross-team coordination, and process. Reporting to the Chief Science Officer, you’ll own key projects yourself while ensuring the broader sub-team operates effectively, grows, and delivers real impact. You’re the person who looks around corners, sees what the business needs, and turns “the business needs X” into “the team builds Y.” You’ll partner closely with Physics Simulation Engineers, Machine Learning Engineers, and peril-specific SMEs to keep our simulation, AI, and geospatial pipelines reliable, observable, and scalable.

✅ Key Responsibilities

• Lead the platform sub-team: set priorities, coordinate execution, and unblock the team to deliver on critical software and infrastructure initiatives.
• Manage and grow the team: run 1:1s and growth conversations, give direct and timely feedback, manage performance, and mentor engineers as the team scales.
• Design, build, and scale core systems spanning physics simulation, AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components.
• Own production pipelines: debug, monitor, and resolve issues to improve uptime and reliability.
• Build and support scalable ML infrastructure: data pipelines, training systems, evaluation frameworks, and production monitoring.
• Design and operate geospatial pipelines that merge heterogeneous spatial datasets into reproducible, production-grade workflows.
• Strengthen CI/CD and infrastructure reliability across simulation, digital-twin, and ML pipelines.
• Drive cross-functional alignment and set direction: coordinate across Applied Science and the business, communicate modeling decisions and tradeoffs, and articulate a multi-year vision for how the team’s work moves the business forward.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

• 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.

🎁 Benefits

The annual base salary range for full-time employees in this position is $240,000 to $295,000 + meaningful Equity Grant.

Compensation decisions are dependent on several factors including, but not limited to, an individual’s qualifications, location where the role is to be performed, internal equity, and alignment with market.