About MentaraIQ

MentaraIQ is building AI-powered “Digital Minds” for creators, educators, coaches, and experts.

Users upload their knowledge — videos, documents, courses, audio, frameworks and other content — and MentaraIQ turns that knowledge into an AI experience others can explore and interact with.

We already have a working product and engineering team. We’re looking for an experienced AI Product Engineer to work alongside our existing developers as we build the next generation of the platform.

Our next major product evolution moves beyond a traditional chatbot interface into an interactive Digital Mind: automatically structured knowledge, explorable concepts, grounded answers, and dynamic response formats such as charts, tables, timelines, frameworks, comparisons and other interactive UI.

What you’ll work onYou’ll help architect and build systems including:

- LLM-powered knowledge extraction and structuring
- RAG and semantic retrieval across creator-uploaded content
- Concept extraction, normalization and clustering
- Automatically generated knowledge graphs / “Digital Minds”
- Source attribution and grounded AI responses
- Applying creator knowledge to a user’s specific situation or context
- Structured LLM outputs for dynamic frontend rendering
- AI-selected response formats such as:
- charts
- tables / data grids
- comparisons
- frameworks
- timelines
- scorecards
- process flows
- source/media cards
- Reliable JSON/schema-constrained model outputs
- Streaming and progressive AI responses
- Caching and preprocessing for fast user experiences
- Frontend integration of AI-generated structured data
- Performance, fallbacks and failure handling
- Evaluating and improving answer grounding / retrieval quality

You’ll work closely with our existing developers rather than replacing them.



Example of the type of problem you’d help solveA creator uploads 100 pieces of content.

MentaraIQ should automatically be able to:

1. Understand their knowledge

Identify major areas of expertise, concepts and relationships.

2. Build an explorable Digital Mind For example:

Marketing → Paid Acquisition → Creative Testing → CAC → Scaling3. Answer questions using only that creator’s knowledge

With clear source attribution.

4. Apply that knowledge to user context For example:

“I have a $20k marketing budget and a new Shopify brand. Based on this creator’s framework, what would you recommend?”5. Choose the best format for the answer

Instead of always returning paragraphs, the system might return:

explanation + comparison table + chart + framework + sourcesThe frontend renders those using reusable Mentara components.

That is the type of AI/product engineering problem we want you thinking about.

Technical experience we’re looking forStrong experience in several of the following:

- LLM APIs and production AI systems
- Retrieval-Augmented Generation (RAG)
- Embeddings / vector databases
- Semantic search
- Knowledge graphs or hierarchical content systems
- Structured / schema-constrained LLM outputs
- Prompt and context engineering
- LLM evaluation and grounding
- Python and/or TypeScript
- React / Next.js
- Backend APIs and databases
- Streaming AI responses
- Data visualization
- Modern cloud infrastructure





Experience with frameworks such as LangChain, LlamaIndex or similar is useful, but we care far more about whether you understand the underlying architecture than which framework you use. Who we’re looking forYou’re probably a strong fit if you:
- Have built real AI products, not just demos
- Understand how to make LLM systems reliable in production
- Can think across backend, AI and frontend product behavior
- Move extremely quickly
- Are comfortable entering an existing codebase
- Can make pragmatic architecture decisions rather than overengineering
- Know when deterministic software should handle something instead of an LLM
- Care about latency and user experience
- Can explain technical tradeoffs clearly
- Enjoy rapid founder-led product development

Particularly valuable experienceBig plus if you’ve built:
- AI knowledge bases
- AI education products
- RAG systems at scale
- Agents / structured tool-calling systems
- Knowledge graphs
- Agile generative UI
- Interactive data or visualization products
- Creator / course / content platforms

What this is NOTWe are not looking primarily for:

- an ML researcher training foundation models
- a prompt engineer with limited software engineering experience
- someone who only builds prototypes in notebooks
- a frontend engineer with no experience building LLM systems

We need somebody who can help ship the actual product. EngagementPart-time / contract initially

Immediate start preferred.

You’ll work directly with the founder and current engineering team.

We operate in fast build cycles with short daily huddles, clear daily deliverables and working demos rather than long development cycles.

- There is potential for the role to expand depending on fit.

📌 Software Engineer (Vancouver)
🏢 MentaraIQ
📍 Vancouver