Why Join Stand: 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.
Location: Onsite in Jackson Square, San Francisco.
Compensation: $30/hr. Targeting 40/hrs a week. We do not cover relocation or lodging stipends.
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
What You’ll Do:
Depending on your area of expertise and the needs of the team, you may contribute to:
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 velocityYou’ll be working on real production systems — not toy problems — with mentorship and guidance from experienced engineers.
Required Skills:
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 Have Skills:
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
Equal Opportunity Employment
Stand is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. We believe that diversity enriches the workplace, and we are committed to growing our team with the most talented and passionate people from every community.
We are committed to providing reasonable accommodations for qualified individuals. If you require assistance
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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