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.

Role Summary:

This ML Engineer role owns tooling surrounding Stand’s data annotation pipeline: computer vision, human-in-the-loop management, quality, and unit economic optimization—the system that feeds every simulation and underwriting decision. The mandate is to improve automation and reduce cost-per-policy while maintaining a strict, instrumented quality floor.

The position begins with deep operational ownership to learn our processes (running the pipeline, QA, annotation team coordination), then transitions into building compounding data science and machine learning systems: quality instrumentation, automated QA, predictive labeling, and computer vision models.

Over time, the role will build a systems-driven, automation-first approach across the entire annotation lifecycle.

Key Responsibilities:

1. Pipeline Operations & Reliability

Own day-to-day annotation pipeline health
Build escalation systems and failure categorization frameworks
Transition execution from manual ops → automated systems

2. Quality Instrumentation

Build validation systems anchored on downstream model metrics

Develop anomaly detection models for annotation
Reduce manual QA burden through automation

3. Vendor & Annotator Performance

Define performance metrics (quality, throughput)
Build training systems and feedback loops
Scale vendor operations

4. Computer Vision & ML Systems

Own the automation roadmap
2D/3D modeling, characterization, reconstruction
Predictive labeling and difficulty routing systems

Core Competencies (Required):

Strong ML and Data Science fundamentals with production experience
Computer vision (2D/3D, generative models, point clouds, remote sensing)
Experience building systems for annotation and data labeling
Comfort operating and improving real-world pipelines
Structured problem-solving and systems thinking
Clear written communication and cross-functional collaboration
High ownership mindset with weekly metric accountability

Nice-to-Have Skills:

Predictive labeling, self-supervised or HITL systems
Multimodal ML or agentic workflows (LLMs + CV)
Experience writing SOPs, dashboards, and operational tooling
Experience managing annotation vendors or distributed teams
Temporal change detection or geospatial data systems
Exposure to insurance, risk modeling, or climate data

Compensation:

The annual base salary range for full-time employees in this position is $185,000 to $235,000 + 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
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.

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