Simulation Platform Engineering Intern

Stand Insurance • San Francisco, CA

1d • $30 - $30 • Onsite

About The Position

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.

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

Background: 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.
Background: Most property insurers assess wildfire risk using broad proxies, backward-looking loss data, and simplified hazard scores. While sufficient for portfolio pricing, these tools break down at the property level—where homeowners need to understand what actually drives loss and what actions meaningfully reduce it.
Stand operates from first principles. We simulate fire behavior and structure exposure using deterministic, physics-based models, then validate those models against controlled fire experiments. The result is a shift from correlation-based pricing to a causal understanding of wildfire risk and mitigation effectiveness.
Experiments and simulation validation are therefore foundational to our work. Converting experimental results into clean, well-documented, simulation-ready datasets is critical to ensuring our models are accurate, trustworthy, and actionable for underwriting and mitigation decisions.
Role Summary: As a Simulation Platform Engineering Intern, you’ll support and extend the simulation and digital twin platform that underpins Stand’s modeling workflows. You’ll work closely with Simulation Engineers, Machine Learning Engineers, and domain experts to improve reliability, scalability, and data quality across our pipelines.
This role is ideal for someone who wants hands-on experience building real systems, enjoys learning across disciplines, and is excited by zero-to-one infrastructure in a fast-moving startup environment.
What You’ll Gain:
By the end of this internship, you will have:
Hands-on experience with production-grade simulation systems used to model real catastrophic risk at the individual-property level — not academic demos or side projects
Practical exposure to how physics-based simulation, geospatial data, and machine learning interact inside a single operational platform
Experience working on zero-to-one infrastructure in a fast-moving startup, including the tradeoffs involved in building reliable systems under real constraints
A deeper understanding of digital twin pipelines, from raw geospatial and vendor data through simulation, post-processing, and downstream decision-making
Improved engineering judgment from debugging complex pipelines, improving observability, and learning how to make systems more robust at scale
Direct mentorship from experienced engineers across simulation, ML, and infrastructure, with regular feedback and technical context
A clear view into how deep technical work translates into real-world impact, influencing underwriting decisions and physical risk mitigation — not just models on paper

Requirements

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, Physics, or a related technical field
• Strong programming fundamentals and an interest in building reliable, data-driven systems
• Comfort working with data pipelines, simulations, or infrastructure (academic or personal projects count)
• Curiosity about simulation, digital twins, geospatial data, or machine learning — you don’t need to be an expert yet
• Ability to debug problems methodically and learn new tools quickly
• Strong collaboration and communication skills
• High ownership mindset and willingness to dive into unfamiliar territory

Nice To Haves

• Exposure to physics-based simulation, numerical methods, or modeling
• Experience with geospatial data (e.g., raster/vector data, LiDAR, DEMs)
• Familiarity with cloud infrastructure, CI/CD, or containerized workflows
• Experience working on research, startup, or open-ended technical projects
• Interest in climate risk, natural hazards, or resilience engineering

Responsibilities

• Supporting production simulation pipelines by helping debug issues, improve observability, and increase reliability
• Assisting with CI/CD, testing, and infrastructure improvements for simulation, digital twin, and ML workflows
• Building or extending annotation and quality-control tooling for digital twins, including ML-assisted workflows
• Contributing to new digital twin features related to wind, flood, wildfire, or other catastrophic perils
• Helping integrate new peril pipelines end to end, from geospatial and vendor data ingestion through simulation and post-processing
• Supporting geospatial data pipelines that merge heterogeneous spatial datasets into reproducible workflows
• Collaborating with ML engineers to support training and inference pipelines
• Documenting pipelines, data assumptions, and operational learnings to improve team velocity

What This Job Offers

• Job Type: Full-time
• Career Level: Intern