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# Software Engineer

- **Company:** AUGMODO INC.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $150,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Regular Expressions, Spatial Data Infrastructures, Management of Software Versions, Large Language Models, Prompt Engineering, Lidar
- **Published:** July 11, 2026
- **Apply:** https://jobs.ashbyhq.com/augmodo/51c848a9-c586-4ac6-9253-6e9f52578d35

## About the Role

* The "Data Plumber" Mindset: Extensive experience building reliable, staged pipelines for complex ingestion tasks.
 * Entity Resolution & Conflation: You have experience merging disparate, messy data sources into a unified "Golden Record."
 * Pragmatic AI Experience: You are comfortable with LLMs and prompt engineering but remain skeptical enough to know when a simple regex or bounded heuristic is the better engineering choice.
 * Engineering Rigor: You believe in versioning everything-from your code to your data schemas.

Bonus Points

 * Physical Product Domain: Experience in retail, logistics, or supply chain (handling SKUs, UPCs, and physical inventory data).
 * Spatial Data: Experience working with data that has a physical or geographic component (GIS, LIDAR, or Computer Vision metadata).

## Description

We are looking for a Software Engineer to own the logic behind our Spatial Data Ingestion & Conflation Engine. You will build the pipelines that take raw, complex data from the edge and transform it into high-fidelity insights for our customers. This role is about precision at scale: ensuring that when a brand or retailer looks at our data, it is versioned, reliable, and "production-ready.", * Build Staged Pipelines: Design and maintain versioned data pipelines that handle the ingestion and conflation of complex retail data (e.g., matching computer vision detections to master product catalogs).

 * High-Fidelity Output: Ensure that data outputs surfaced to consumer-facing dashboards meet a high quality bar for accuracy and reliability.
 * Hybrid AI Strategy: Implement a "right tool for the job" approach-building robust rules-based normalization and RegEx logic for bounded tasks, while strategically integrating LLMs to solve high-complexity data matching and entity resolution.
 * Scale & Cost Management: Architect solutions that are mindful of the massive scale of retail environments, ensuring AI integrations are cost-effective and performant.
 * Versioned Data Evolution: Manage the lifecycle of data schema and pipeline logic to allow for rapid iteration without breaking downstream consumer insights.


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