Simulation Platform Engineer

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: The Simulation Platform Engineer owns and extends the simulation and digital twin platform that underpins Stand’s modeling workflows. This role spans production simulation pipeline development and support, infrastructure and CI/CD reliability, geospatial data processing, annotation and QC systems, and the development of new digital twin capabilities to support the expansion of the business into new geographies and perils, e.g., hurricane.

You will work closely with Physics Simulation Engineers, Machine Learning Engineers, and peril-specific SMEs to ensure our simulation, AI, and geospatial pipelines are reliable, observable, and scalable.

This position is ideal for someone who thrives in fast-paced environments, owns outcomes end to end, collaborates effectively across disciplines, and is energized by building from zero to one.

What You’ll Do:

• Own and support production simulation pipelines, debugging, monitoring, and resolving issues across wildfire simulation and related workflows to improve uptime and MTTR.

• Strengthen CI/CD and infrastructure reliability across simulation, digital twin, and ML pipelines through automated testing, safe deployments, and compute efficiency.

• Build and operate annotation and QC systems for digital twins, improving quality and reducing manual effort via ML-assisted workflows.

• Develop digital twin capabilities, including multiphysics-adjacent features for wind, flood, and other perils.

• Integrate new peril pipelines end to end, across multiple scales, connecting preprocessing, vendor and geospatial data, simulation workflows, and post-processing modules.

• Partner with MLEs on AI pipelines, supporting training, inference, and continuous improvement (e.g., FireNet).

• Design and operate geospatial pipelines, merging heterogeneous spatial datasets into reproducible, production-grade workflows.

• Communicate clearly across teams on pipeline stability, data quality, and failures to enable fast iteration.

What We’re Looking For:

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

• Proven ability to debug distributed, data-heavy systems, especially those 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 practices.

• Comfort working at the intersection of simulation, digital twins, and ML, even if your background leans more heavily toward one area.

• Experience designing reproducible workflows, with versioned datasets, scripted transformations, and auditable pipelines.

• Ability to collaborate directly with simulation engineers, MLEs, and domain SMEs, translating modeling needs into robust platform capabilities.

• High ownership and operational rigor, treating pipelines and platforms as long-lived products, not one-off projects.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, Physics, or a related technical field.

• 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 supporting physics-based or simulation-heavy workflows (e.g., CFD, multiphysics, or digital twin systems) in production, with strong physical simulation mathematical intuition.

• Hands-on experience with geospatial data stacks, including LiDAR, DEMs, and digital twin manipulation.

• Experience working directly with business units or product teams on customer or client-facing technology.

• Experience collaborating with MLEs on training pipelines and dataset construction for large-scale models.

• Proven ability to operate effectively in startup environments, balancing rapid delivery with long-term platform reliability and high ownership.

Compensation:

The annual base salary range for full-time employees in this position is $180,000 to $210,000 + 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.

Work Authorization

Candidates must be authorized to work in the U.S. Stand does not sponsor new work visas for this role. 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.