Senior AI/ML Data Model Architect

About Sapience AISapience 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. Where this role sits 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. Why this role existsAI 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. What you will own (Areas of Responsibility) You hold seven areas of responsibility across the data and model layer. Each one is yours to set direction on and be accountable for.1. Data modeling and schema architecture 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.2. Representations for models and reasoning 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.3. Data flow and lineage 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.4. Consistency and quality by design 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.5. Governance, privacy, and trust 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.6. Partnership across data and AI teams 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.7. Evolution and scale Evolve the data models as the platform, communities, and AI systems grow. Design for scale and for change without constant rework. Keep the foundation coherent through each GR/GA milestone. AI-augmented ways of working You architect the data that AI depends on, and you use AI to do it, to explore schema options, reason about representations, and move faster, while you own the coherence, quality, and trustworthiness of the design. The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions. What this role is not To keep the boundary clear: This is not a pipeline-implementation-only role. You own the architecture and models, partnering with engineers who build the pipelines. This is not a knowledge-graph-only role. You own data models across models, retrieval, and the graph, in partnership with graph engineering. This is not a business-analytics role. You architect data for AI systems, not reporting and dashboards. This is not a research role. You are accountable for a production data foundation, not open-ended exploration.

We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway. Sapience AI is an equal opportunity employer. We are committed to a workplace where everyone, regardless of background, has a voice in building what comes next. Compensation

Generous health and wellness benefits Sapience AI is an equal opportunity employer. We do not discriminate on the basis of gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. If you need an accommodation to complete our application process, let your recruiter know.