Salary
💰 CA$123,600 - CA$187,900 per year
Tech Stack
DjangoPythonReactTypeScript
About the role
- Prototype new AI features: build and ship experimental conversational interfaces and refine quickly based on user feedback.
- Build production-ready features: take prototypes from concept to reliable production systems that scale to thousands of users.
- Design and ship new interaction modes: implement frontend components and supporting APIs for natural language commands and guided conversations.
- Create AI-powered workflows: build end-to-end features where AI agents execute multi-step marketing tasks, including conversation state management, React UI components, and real-time user feedback.
- Collaborate on architecture: contribute to technical planning for how AI experiences integrate with Hive’s platform, focusing on user-facing product impact.
- Work across the stack: primarily React and Typescript, with some backend work in Python/Django to build APIs and connect to AI systems.
- Integrate AI systems with high-volume user and transactional data and marketing channels to drive measurable outcomes.
Requirements
- 3+ years building modern web applications with strong experience in React and Typescript, and comfort working with Python/Django on the backend.
- Hands-on experience building polished user-facing features, with an eye for detail and usability.
- Curiosity about AI systems and excitement to experiment with LLM tools and apply them to real products.
- Product sense: ability to take a vague problem and turn it into a clear, intuitive feature.
- Experience working closely with Product and Design to build features and explore new interaction patterns.
- Ability to clearly explain technical decisions, tradeoffs, and approaches to others.
- Comfort working in a fast-moving environment where best practices are still being defined.
- Strong async communication and collaboration skills in a remote team.
- Must be based in Canada and have legal Canadian work authorization for this role.
- Bonus (nice-to-have): shipped features that use AI in production; experimented with LLM frameworks like LangChain, LangGraph, or semantic kernel; experience with vector databases (Qdrant, Pinecone, Weaviate) or RAG patterns; experience in marketing tech or SaaS.