Physics Simulation Team Lead
$210k - $250k USD
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.
The Opportunity: As a Physics Simulation Team Lead on the Applied Science team, you will lead the development of Stand’s physics-based simulation capabilities across wildfire and future perils. This role begins as a hands-on technical lead and evolves into a people leader as the team scales.
Physics-based simulation is a core pillar at Stand. You will help transform traditionally high-cost, bespoke engineering workflows into scalable, validated, production-ready systems that directly power real-world risk decisions and drive tangible business outcomes.
You will remain deeply embedded in CFD, FEA, and multiphysics solvers, while shaping how simulations connect across scales, inform probabilistic risk models, and integrate with AI-driven acceleration techniques.
This position is ideal for someone who thrives in fast-paced environments, creates structure from ambiguity, owns outcomes end to end, collaborates effectively across disciplines, and is energized by building from zero to one.
What You’ll Do:
Act as the functional technical lead for the growing physics simulation sub-team within the Applied Science group, coordinating work across scales and perils while establishing and pioneering workflows, modeling standards, and validation practices.
Own the overall physics simulation roadmap, including solver development, model tuning, and validation, as well as prioritizing modeling investments across wildfire and future perils based on feasibility, impact, and business needs.
Partner with recruiting to build the simulation sub-team, contributing to interviewing and onboarding early hires and laying the foundation for player-coach leadership as the team scales.
Design and lead expansion into new perils (e.g., wind, flood), including uncertainty scoping, validation milestones, and ownership swimlanes—standing up wholesale new engineering workflows end-to-end (necessitating getting hands dirty diving deep and learning quickly
Guide the development of probabilistic risk mechanisms from deterministic physics models using computationally efficient approaches (e.g., surrogate or reduced-order methods).
Collaborate closely across Applied Science and the full company, including with ML engineers and product teams, and communicate modeling decisions and tradeoffs clearly to leadership and partners.
What We’re Looking For:
PhD or Master’s degree in a relevant field, with 5+ years of hands-on experience leading and executing CFD-based simulations, including design, execution, and validation.
Experience as a technical lead, managing projects or teams in highly dynamic, fast-paced environments.
Strong attention to detail and succinct communication skills, with a demonstrated ability to set technical direction and prioritize modeling work, balancing research depth, delivery timelines, and business impact.
Proficiency with Fortran-based solvers, including running, modifying, and extending existing codebases; fluency in C++, Python, or similar languages for scripting, integration, and tooling; industry experience working with high-performance compute and parallel processing workflows
Strong modeling mathematical intuition - able to make sound tradeoffs between fidelity, runtime, and uncertainty, and to identify the highest-leverage improvements without exhaustive experimentation.
Highly self-motivated, with a “run-through walls to get the problem solved” method, self-directed, proactive, and adaptable; comfortable operating in fast-paced, ambiguous environments where problems, interfaces, and priorities evolve over time.
Preferred Qualifications:
Experience applying physics-based simulation to natural hazards, climate, or risk modeling (e.g., wildfire, wind, flood).
Experience working directly with business units or product teams on customer or client-facing technology.
Prior experience as a people manager in startup or high-growth environments.
Experience designing and owning multiscale or multiphysics simulation pipelines, including coordinating validation efforts across models, data sources, and teams.
Hands-on experience with reduced-order modeling, surrogate models, or uncertainty quantification techniques.
Experience integrating physics-based models with machine learning systems for acceleration or inference.
Compensation
The annual base salary range for full-time employees in this position is $210,000 to $250,000 + meaningful Equity Grant.
Compensation decisions are based on several factors, including an individual’s qualifications, the location where the role is performed, internal equity, and alignment with market data.
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.