Software Engineer, Data Intelligence

Augmodo•United States, CA

5d•$150,000 - $200,000

About The Position

Augmodo is building the "operating system for the physical shelf." We use spatial computing and wearable AI to provide real-time, store-level insights that were previously impossible to capture. We are moving fast, and our data needs to move even faster—while remaining hyper-accurate. 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."

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.

Nice To Haves

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).

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.