Applied Science Platform Lead

Posted 20 Days Ago
Be an Early Applicant
San Francisco, CA, USA
Hybrid
200K-240K Annually
Senior level
Insurance • Financial Services
Insuring the world's climate-impacted properties.
The Role
Lead the Applied Science platform sub-team to design, build, and operate production-grade simulation, ML, and geospatial pipelines. Own technical direction, CI/CD, cluster compute, workflow orchestration, reliability, and cross-functional alignment while doing hands-on engineering and mentoring engineers.
Summary Generated by Built In

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, our Stand World Model.

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

  • Experience leading engineers and technical initiatives, delivering complex projects through others as well as through direct individual contribution.

  • 5+ years building production-grade data, simulation, or modeling pipelines with end-to-end automation, monitoring, and alerting.

  • Strong project ownership and execution: planning, prioritization, stakeholder coordination, and delivery from concept through production.

  • Proven ability to debug distributed, data-heavy systems — spanning APIs, preprocessing, modeling, and downstream consumers.

  • Hands-on experience with cloud infrastructure and CI/CD, including infrastructure-as-code, automated testing, and reliable deployment.

  • Experience with cluster compute and job scheduling (e.g., Slurm and/or Kubernetes) and workflow orchestration (e.g., Prefect or Apache Airflow).

  • Comfort working across backend, AI pipelines, simulations, and digital twins, even if your background leans toward one area.

  • Ability to connect technical work to business objectives and customer impact, and to translate SME needs into robust platform capabilities.

  • Strong, succinct communication, and the judgment to balance research depth, delivery timelines, and business 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 $200,000 to $240,000 + 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

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.

Skills Required

  • Experience leading engineers and technical initiatives, delivering complex projects through others and individual contribution
  • 5+ years building production-grade data, simulation, or modeling pipelines with end-to-end automation, monitoring, and alerting
  • Strong project ownership and execution: planning, prioritization, stakeholder coordination, and delivery from concept through production
  • Proven ability to debug distributed, data-heavy systems across APIs, preprocessing, modeling, and downstream consumers
  • Hands-on experience with cloud infrastructure and CI/CD, including infrastructure-as-code and automated testing
  • Experience with cluster compute and job scheduling (e.g., Slurm and/or Kubernetes) and workflow orchestration (e.g., Prefect or Apache Airflow)
  • Comfort working across backend, AI pipelines, simulations, and digital twins
  • Ability to connect technical work to business objectives and translate SME needs into platform capabilities
  • Strong, succinct communication and judgment to balance research depth, delivery timelines, and business impact
  • Authorization to work in the U.S.; employer does not sponsor new work visas (limited visa transfer/exception cases noted)
  • Prior people-management experience 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
  • Knowledge of geospatial, remote-sensing, or Earth-observation datasets and systems
  • Experience collaborating with MLEs on training pipelines and dataset construction for large-scale models
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The Company
San Francisco, , California
11 Employees
Year Founded: 2024

What We Do

We protect and insure homes — wildfire, hurricane, and beyond using physics and AI to understand the true risk of your property and help make your home insurable.

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