Job description

ABOUT AUGMODO 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. THE CHALLENGE 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." KEY 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. REQUIRED QUALIFICATIONS - 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). Augmodo offers benefits inclusive of medical, dental, vision and 401k The salary range for this role is $150,000 - 200,000 USD +equity Augmodo is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.

Requirements

Must-haves

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.
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.
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).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k