Applied AI Engineer; JavaScript, Intermediate to Senior, Remote

Heads-up: this job will only be open until 5pm PT on Sunday, September 13, 2026 so please apply right away if interested.

Intermediate to Senior | Remote in Canada | 6 months Term Employee | 32-36 Hours per Week (Mon-Thu) | Hiring Budget: $50/hr - $65/hr

Hiya! We're Simpli City , a Vancouver-based Gov Tech startup building technology with purpose. Our core product is a composable content management system (CMS) that unifies municipal digital services, transforming a municipality's website from a static library into a connected "front door" for the community. We are a small but mighty team that values diversity, curiosity, and the drive to continuously learn and improve.

The Opportunity Moving a municipality onto our platform means converting thousands of web pages, documents, and images into structured content, and today most of that work is done by hand. We are hiring an Applied AI Engineer for a six-month project to change that. You will design and build an AI and automation pipeline that reads a client's existing website content, interprets and classifies it, generates accurate metadata, and maps it into structured records and machine-readable JSON, with validation of output quality s is a hands-on applied engineering role, not a research one.

You will work directly with our CTO, Ian Sim, who directs the architecture, alongside our fullstack engineer and our QA/content specialist. The automation pipeline will be proven on real municipal website migrations under real deadlines, and the goal at the end of the project is a repeatable pipeline that any of our team members can run.

• Reusable migration scaffolding: A single, repeatable structure for migration tracking, content inventory, and site scraping that works across all municipalities.
• AI generation for templated records:
  News, events, meetings, facilities, bylaws, and contacts, which together make up roughly half of a municipality's content by volume.
• AI mapping to content models:
  Converting unstructured and semi-structured source material into structured records that conform to a defined schema. The mapping is not deterministic, and each client layers its own rules and vocabulary on top, so this is the hardest and most interesting part of the project.
• Validation at scale:
  Tooling that confirms AI output quality quickly, so quality checking keeps pace with volume.
• An MCP server:
  Making the pipeline's capabilities reachable in a standard way (Model Context Protocol) by AI assistants, our platform, and downstream systems.
• Internal application:
  Build an internal tool that takes advantage of the MCP and pipeline for any Simpli City staff member to be able to migrate content themselves.

• AI: LLM APIs and coding agents, prompt and context engineering, model selection and fine-tuning, structured output with JSON schemas, and evaluating output quality. We currently use Claude Code as a team but it may make sense to explore other frontier models, or open source ones, or all the above.
• Platform:
  JavaScript (Node.js), ReactJS, REST APIs, composable/headless CMS architecture, and Microsoft Azure (Canadian cloud-hosted).
• Data:
  Unstructured and semi-structured web content, HTML and document parsing, content classification, and metadata extraction.
• Tools:
  Git (Bitbucket & Git Hub), Jira, Slack, Notion, and Microsoft 365/One Drive.

An applied AI builder with 3+ years of software engineering experience:
You have shipped real software, and you have built things with LLMs beyond chat: pipelines, agents, extraction or classification systems, or evaluation harnesses.

• A strong JavaScript engineer:
  You bring strong JavaScript and Node.js expertise, since the pipeline and its tooling are built in JavaScript, from command-line scripts to web utilities.
• An engineer who ships tools, not demos:
  You know the difference between a supervised script that works on one machine and a repeatable tool a teammate can run without you, and you build for that last case.
• A pragmatist about model quality:
  You measure output quality instead of eyeballing it, and you know when to reach for prompting, fine-tuning, or plain deterministic code.
• Comfortable with messy source data:
  Real municipal websites are inconsistent. You are not put off by tangled HTML, scanned PDFs, and content that follows no schema.
• A collaborative problem‑solver:
  You work closely with our CTO, engineers, QA/content specialist, and other team members to make technical recommendations. You are…