Spatial Data Ingestion Engineer for AI Pipelines

Augmodo; Seattle (WA); On-site; USD 150,000 - 200,000; Full time

Job summary

Augmodo is building the operating system for the physical shelf and seeks a Software Engineer to own the Spatial Data Ingestion & Conflation Engine. You will design pipelines that transform raw edge data into high-fidelity, production-ready insights for retailers and brands.

You will balance rules-based normalization with LLMs to solve complex data matching, ensure versioned data evolution, and design scalable, cost-aware data architectures for mass retail environments.

Qualifications

• Experience building reliable, staged pipelines for complex ingestion tasks.
• Experience merging disparate data into a Golden Record.
• Comfort with LLMs and prompt engineering, balanced with simple regex or heuristics.
• Engineering rigor with versioning of code and data schemas.

Responsibilities

• Build Staged Pipelines: Design and maintain versioned data pipelines that handle ingestion and conflation of complex retail data.
• High-Fidelity Output: Ensure data outputs meet high quality for accuracy and reliability.
• Hybrid AI Strategy: Implement rules-based normalization and Regex logic, while integrating LLMs for complex matching and entity resolution.
• Scale & Cost Management: Architect cost-effective, performant solutions for large-scale retail environments.
• Versioned Data Evolution: Manage data schema and pipeline lifecycle for rapid iteration without breaking downstream insights.

Skills

Data pipelines

Entity resolution

Pragmatic AI

Engineering rigor

Tools

Regex

LLMs

Version control

Job description

Augmodo is building the operating system for the physical shelf and seeks a Software Engineer to own the Spatial Data Ingestion & Conflation Engine. You will design pipelines that transform raw edge data into high-fidelity, production-ready insights for retailers and brands.

You will balance rules-based normalization with LLMs to solve complex data matching, ensure versioned data evolution, and design scalable, cost-aware data architectures for mass retail environments.