Software Engineer, Data Intelligence

Augmodo is building an operating system for the physical shelf using spatial computing and wearable AI to provide real-time, store-level insights. The Software Engineer, Data Intelligence will own the Spatial Data Ingestion & Conflation Engine by building reliable, versioned pipelines that transform complex edge data into accurate, production-ready customer insights. The role also involves entity resolution, pragmatic use of rules and LLMs, and designing scalable, cost-effective data systems.

Responsibilities:

• 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

Requirements:

• 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
• 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)