Applied Science DevOps Lead

Stand Insurance•San Francisco, CA

13h•$240,000 - $295,000

About The Position

Stand is building a property-protection platform that uses physics and AI to model catastrophic risk at the asset level and automate underwriting and mitigation. Its platform simulates the effects of catastrophes on individual properties and supports insurance operations with a scalable risk engine called the Stand World Model. The Applied Science DevOps Lead leads the platform sub-team within Applied Science. The team builds, deploys, and supports physics simulations, digital twins, AI/ML models, and spatial intelligence. This player-coach role sets technical direction and plans and delivers platform initiatives in collaboration with Applied Science and companywide stakeholders. The work spans production AI training and inference, simulation pipelines, infrastructure and CI/CD reliability, geospatial data processing, annotation and quality-control systems, and digital-twin capabilities supporting expansion into new geographies and perils. Reporting to the Chief Science Officer, the lead combines hands-on technical contribution with people management, project planning, cross-team coordination, and process development. They own key projects while ensuring the sub-team operates effectively, develops, and delivers impact. They partner with Physics Simulation Engineers, Machine Learning Engineers, and peril-specific subject-matter experts to keep simulation, AI, and geospatial pipelines reliable, observable, and scalable.

Requirements

Experience 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.
At least 5 years building production-grade data, simulation, or modeling pipelines with end-to-end automation, monitoring, and alerting.
Strong DevOps ownership, including cloud infrastructure, infrastructure-as-code, containerization, CI/CD, automated testing, observability, and incident response for systems the team operates.
Depth in distributed, data-heavy systems, including debugging across APIs, preprocessing, modeling, and downstream consumers.
Experience with cluster compute such as SLURM or Kubernetes and orchestration tools such as Prefect or Airflow.
Business judgment and concise communication, including translating subject-matter expert needs into platform capabilities and balancing research depth, timelines, and impact.

Nice To Haves

Startup or zero-to-one technology development experience.
Hands-on data and ML infrastructure experience, including ETL over large heterogeneous datasets, dataset construction, GPU training and inference, evaluation frameworks, and production model monitoring.
Breadth across backend systems, AI pipelines, simulations, and digital twins, even if experience is concentrated in one area.
Experience supporting physics-based or simulation-heavy workflows, such as CFD, multiphysics, or 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.

Responsibilities

Lead the platform sub-team by setting priorities, coordinating execution, and unblocking delivery of software and infrastructure initiatives.
Manage and grow the team through one-to-one meetings, growth conversations, timely feedback, performance management, and engineering mentorship.
Design, build, and scale systems for physics simulation, AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components.
Own production pipelines by debugging, monitoring, and resolving issues to improve uptime and reliability.
Build and support scalable ML infrastructure, including data pipelines, training systems, evaluation frameworks, and production monitoring.
Design and operate geospatial pipelines that combine 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 across Applied Science and the business, communicate modeling decisions and tradeoffs, and articulate a multi-year vision for the team’s work.
Translate business needs into platform capabilities and balance research depth, timelines, and impact.