Senior AI​/ML Data Model Architect

Seattle, WA or West Coast Remote Preferred

Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share.

The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that.

Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves.

Let’s achieve more, together.

This role owns the shape of the data that AI learns from and reasons over. You design the data models, schemas, and representations that connect a community’s knowledge to the models, the KO graph, and the COGENT architecture.

You work where data architecture meets machine learning: how knowledge is modeled, how features and embeddings are represented, and how data flows from raw sources to the systems that reason over it.

You are the person who makes sure the data foundation is coherent, so everything built on top of it, from retrieval to reasoning to models, has something solid and consistent to stand on.

AI is only as good as the data it learns from and reasons over. When data models are inconsistent or poorly designed, every system downstream inherits the mess, and trust erodes.

Modeling data for collective intelligence is hard: it has to serve models, graphs, retrieval, and reasoning at once, across many communities, without fragmenting.

The Senior AI/ML Data Model Architect owns that foundation. You design the data models and representations that keep the platform coherent and let every AI system built on them work well.

You hold seven areas of responsibility across the data and model layer. Each one is yours to set direction on and be accountable for.

• Design the data models and schemas that represent a community’s knowledge for AI.
• Keep the models coherent across communities and across the systems that use them.
• Balance flexibility with the structure that reasoning and trust require.

• Design how knowledge is represented for models, retrieval, and the KO graph.
• Own the feature and embedding representations that AI systems depend on.
• Align representations so neural and symbolic systems can work together.

• Architect how data flows from raw sources to models, the graph, and reasoning.
• Own lineage and provenance so data can be traced and trusted.
• Make the flow observable and reliable.

• Set the standards that keep data consistent and high quality across the platform.
• Prevent fragmentation and duplicated, divergent models.
• Make correctness a property of the design.

• Design data models that protect sensitive community knowledge and support governance.
• Build privacy and access into the structure of the data.
• Support the trust members and organizations place in the platform.

• Partner with knowledge graph, applied AI, infrastructure, and research on shared representations.
• Align teams around a common data foundation rather than local models.
• Resolve the hard cross-team data questions.

• Evolve the data models as the platform, communities, and AI systems grow.
• Design for…