ML Engineer, Data Pipeline

Responsibilities

• Pipeline Operations & Reliability
• Own day-to-day annotation pipeline health
• Build escalation systems and failure categorization frameworks
• Transition execution from manual ops → automated systems
• Quality Instrumentation
• Build validation systems anchored on downstream model metrics
• Develop anomaly detection models for annotation
• Reduce manual QA burden through automation
• Vendor & Annotator Performance
• Define performance metrics (quality, throughput)
• Build training systems and feedback loops
• Scale vendor operations
• 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

Benefits

Additional Information

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 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. 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.

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