--- title: 'Applied Science Platform Lead at Stand Insurance' canonical: 'https://feeny.ai/job/applied-science-platform-lead-stand-insurance-san-francisco-njdxbdh6a3yh' type: 'job' last_seen: '2026-09-14' --- # Applied Science Platform Lead at Stand Insurance - **Company:** Stand Insurance - **Location:** San Francisco, CA - **Compensation:** $240k–$295k - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-07-08 - **Last confirmed live:** 2026-09-14 - **Apply:** https://jobs.ashbyhq.com/standinsurance/d6867d42-563b-4385-8db9-4acb7757a3f2 ## Job description [Why Join Stand](https://www.standinsurance.com/careers/why-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, our [Stand World Model](https://frontier.standinsurance.com). We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices. Our [leadership team](https://www.standinsurance.com/vision) includes former successful founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight, bringing deep experience in building and scaling high-growth companies. 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. Role Summary: As the Applied Science Platform 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. What You'll Do: - 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. Core Skills (Must-Haves): - Technical leadership and ownership” 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. - Hands-on data and ML infrastructure: ETL over large heterogeneous datasets, dataset construction, GPU training and inference, evaluation frameworks, and production model monitoring. - Depth in distributed, data-heavy systems: debugging across APIs, preprocessing, modeling, and downstream consumers; cluster compute (Slurm, Kubernetes) and orchestration (Prefect, Airflow). - Breadth across backend, AI pipelines, simulations, and digital twins, even if your background leans toward one area. - Business judgment and succinct communication: translate SME needs into platform capabilities and balance research depth, timelines, and impact. Nice to Haves: - Prior people-management experience, especially in high-growth environments. - Startup or zero-to-one technology development experience. - 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. Compensation: 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 data. Benefits: - Above-market Health, Dental, and Vision coverage - Weekly lunch stipend - Flexible time off + holidays - 401(k) plan - Commuter benefits - PAT & MAT Leave - Short-Term and Long-Term Disability - Monthly team gatherings - In-office perks AI in the Interview Process Thoughtful use of AI tools is expected and valued at Stand. Candidates should be prepared to discuss how they use AI, how they evaluate its output, and how it informs their work. Strong communication, sound judgment, and the ability to clearly articulate experience and decision-making remain core requirements. Some parts of the interview process are designed to assess independent thinking, communication, and problem-solving. If you plan to use an AI assistant or LLM during any portion of an interview, please discuss it with your interviewer in advance. Work Authorization Candidates must be authorized to work in the U.S. Stand does not sponsor new work visas. We can consider candidates on TN visas, O-1A visas, or H-1B transfers with three years or more remaining. ## 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. ## About Stand Insurance ## Company Overview - **One-liner**: Stand Insurance uses physics-based simulation and AI to model wildfire, hurricane, and other perils at the individual property level, enabling it to insure homes that traditional carriers reject. - **Entity Type**: Private (raised $80 M in venture funding) - **Headquarters**: San Francisco, California, USA - **Founded**: 2024 - **Founders**: Dan Preston (CEO), Jason Mueller, Sam Shank, Bill Clerico ## Core Business - **Primary industry**: Property & casualty insurance (climate-resilience focused) - **Target customers**: Homeowners in high-risk areas (wildfire, hurricane, earthquake zones); works through brokers and directly - **Mission**: “Protect and insure homes — wildfire, hurricane, and beyond” by proving risk is insurable through science, not ZIP-code averages ## Products & Services - **Stand Insurance (Home Policies)**: Custom insurance policies for high-value properties in wildfire, hurricane, and earthquake zones. Policies are backed by A‑rated reinsurance partners and include risk-mitigation recommendations. - **Stand World Model**: A physics-grade digital twin of every insured property. Uses CFD (computational fluid dynamics) and FEA (finite element analysis) to simulate fire spread, wind, and water at sub‑meter resolution. The model identifies the highest‑leverage mitigations (e.g., defensible space, ember‑resistant vents) and adjusts premiums accordingly. ## Market Standing - **Valuation / Market Cap**: Not disclosed - **Key Metric**: $80 M total funding raised (as of May 2026) - **Notable Investors**: Equal Ventures, Lowercarbon Capital, Eclipse Ventures, Inspired Capital - **Growth Signals**: Growing 20 % month‑over‑month; expanding from California into Florida (SE hurricane products); team of ~50 people; hiring across science, engineering, insurance, and operations ## Competitive Advantages - **Physics‑first risk model**: Simulates actual fire, wind, and water behavior on each parcel (not statistical averages), enabling coverage for homes others won’t touch. - **Field‑verified data**: Every policy includes a property assessment that captures real‑world details models miss. - **AI‑native stack**: Uses frontier models and coding agents across underwriting, engineering, and operations; collapses week‑long analyses into seconds. - **Founding team**: Serial entrepreneurs who built Metromile (Nasdaq), Policygenius (acquired), HotelTonight (acquired by Airbnb), and WePay (acquired by JPMorgan). ## Strategic Focus - **Geographic expansion**: Entering Florida (hurricane) after establishing California (wildfire); planning additional states. - **Talent scaling**: Hiring ahead of 20 % monthly growth, especially in physics simulation, ML, and insurance product roles. - **Product development**: Deepening the “Resilience as a product” model — helping homeowners make properties less vulnerable and rewarding them with lower premiums. ## Why Work Here - **Hybrid work model**: Offices in San Francisco and Florida; roles are hybrid (some in‑office, some remote flexibility). - **Equity & compensation**: All roles offer equity; salary ranges listed (e.g., $150 K–$210 K for senior roles). - **Small team, outsized impact**: ~50 people today; AI multiplies output, so each person owns a large surface area from day one. - **Cutting‑edge tech**: Work on GPU/HPC infrastructure, physics simulations, and the latest AI tools; the company adopts new tools “the week they ship.” - **Mission‑driven**: Tackling the hardest climate‑risk problem in insurance — making the most exposed homes insurable. ## Sources 1. [standinsurance.com](https://www.standinsurance.com/) 2. [standinsurance.com/careers/](https://www.standinsurance.com/careers/) 3. [standinsurance.com/careers/why-stand/](https://www.standinsurance.com/careers/why-stand/) 4. [standinsurance.com/vision/](https://www.standinsurance.com/vision/) 5. [builtin.com](https://builtin.com/company/stand-standinsurancecom) ## Other roles at Stand Insurance - [Client Concierge Specialist](https://feeny.ai/job/client-concierge-specialist-stand-insurance-austin-zr6dk4216vbj) — Austin, TX - [Member of the Technical Staff - Data Engineer](https://feeny.ai/job/member-of-the-technical-staff-data-engineer-stand-insurance-san-francisco-1jfrcgs78qsr) — San Francisco, CA - [Member of the Technical Staff - Agentic Engineer](https://feeny.ai/job/member-of-the-technical-staff-agentic-engineer-stand-insurance-san-francisco-z776hztygmt1) — San Francisco, CA - [Machine Learning Engineer - Multimodal Modeling](https://feeny.ai/job/machine-learning-engineer-multimodal-modeling-stand-insurance-san-francisco-nnj8x0j03mgc) — San Francisco, CA - [Head of Marketing](https://feeny.ai/job/head-of-marketing-stand-insurance-san-francisco-sgg0vbyrchya) — San Francisco, CA - [Market Lead](https://feeny.ai/job/market-lead-stand-insurance-san-francisco-4xq7g1e2bjr0) — San Francisco, CA - [Machine Learning Team Lead](https://feeny.ai/job/machine-learning-team-lead-stand-insurance-san-francisco-t5adr64rzf4p) — San Francisco, CA - [Stand Insurance Talent Community](https://feeny.ai/job/stand-insurance-talent-community-stand-insurance-san-francisco-082ymg03r9mk) — San Francisco, CA