---
title: 'Machine Learning Team Lead at Stand Insurance'
canonical: 'https://feeny.ai/job/machine-learning-team-lead-stand-insurance-san-francisco-t5adr64rzf4p'
type: 'job'
last_seen: '2026-09-07'
---

# Machine Learning Team Lead at Stand Insurance

- **Company:** Stand Insurance
- **Location:** San Francisco, CA
- **Compensation:** $270k–$325k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-06-03
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/standinsurance/0e89ca76-3c88-48d2-a885-fdd6aa7b8704/application

**Skills:** Machine Learning, Physics-informed AI, Digital Twins, Computer Vision, Spatial Intelligence, Scientific Computing, Surrogate Modeling, ML Infrastructure, Data Pipelines, Training Systems, Evaluation Frameworks, Production Monitoring, Multimodal Learning, Spatially-aware Architectures, Agentic Systems, LLM-powered Workflows, Geospatial Data, Remote Sensing, Earth Observation

> Lead the Machine Learning Engineering sub-team in developing and deploying AI capabilities including physics-informed ML, digital twins, and computer vision. This player-coach role combines technical leadership, people management, and direct contribution to building scalable risk analytics systems.

## 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 MLE Team Lead on the Applied Science team, you will lead the Machine Learning Engineering sub-team as it develops and deploys Stand's flagship AI capabilities spanning physics-informed machine learning, digital twins, computer vision, and spatial intelligence. You will own the technical direction, planning, and execution of critical AI initiatives, ensuring they align with business priorities, ship on schedule, and deliver measurable outcomes.

This is a player-coach role, combining direct technical work and the leadership work around it: people management, project planning, cross-team coordination, and process. Reporting directly to the Chief Science Officer, you will own key projects yourself while ensuring the broader MLE team is operating effectively, growing, and delivering real impact. You are the person who looks around corners, sees what the business needs, and turns "the business needs X" into "the team builds Y."

You will partner across Applied Science and the business to transform research and emerging technologies into scalable systems that directly influence underwriting, pricing, mitigation, inspection, and customer decision-making.

Key initiatives include:

- Advancing physics-informed, AI-driven solvers and surrogate architectures
- Advancing multimodal models, data augmentation, sensor fusion, and digital twin capabilities
- Driving R&D programs through to validation, deployment, and business adoption
- Building production-ready AI systems that accelerate, automate, and scale risk analytics

What You'll Do:

- Lead the Machine Learning Engineering sub-team, defining priorities, coordinating execution, and unblocking the team to deliver on critical AI initiatives
- Manage and grow the team, running 1-on-1s and growth conversations, giving direct and timely feedback, managing performance, and mentoring engineers as the team scales
- Design, build, and deploy machine learning systems spanning physics-informed AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components
- Own projects end-to-end, from problem definition and prototyping through production deployment, adoption, and ongoing performance
- Extend state-of-the-art models and surrogate architectures to accelerate simulation and risk analytics workflows
- Guide, support, and build scalable ML infrastructure, including data pipelines, training systems, evaluation frameworks, and production monitoring
- Improve how the team works, creating process improvements and maintaining traceability
- Drive cross-functional alignment, coordinating across Applied Science and the business and clearly communicating modeling decisions, tradeoffs, and status
- Set a multi-year vision for the MLE team's impact and articulate how its work moves the business

Core Skills (Must-Haves):

- Proficiency with modern ML tooling and infrastructure
- Experience leading engineers and technical initiatives, delivering complex projects through others as well as through direct individual contribution
- Strong project ownership and execution: planning, prioritization, stakeholder coordination, and delivery of complex technical programs from concept through production
- Experience combining physics-based modeling and machine learning, including simulation, scientific computing, surrogate modeling, and/or physics-informed AI approaches
- Ability to operate across disciplines, connecting technical development to business objectives and customer impact, and articulating those links to the team
- Strong, succinct communication and the judgment to balance research depth, delivery timelines, and business impact
- Highly self-motivated, proactive, and adaptable; comfortable in fast-paced, ambiguous environments where problems, interfaces, and priorities evolve

Nice to Haves:

- Prior experience as a people manager, specifically in high-growth environments
- Experience with computer vision, multimodal learning, or spatially-aware architectures
- Familiarity with building agentic systems and LLM-powered workflows
- Experience in startups or zero-to-one technology development
- Knowledge of geospatial, remote sensing, or Earth observation datasets and systems

Compensation:

The annual base salary range for full-time employees in this position is $270,000 to $325,000 plus 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)

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