Posting record
Open 12+ days
- Hellyeah's site dates the posting
- Ghostless first sees this listing
- Last checked: still up
How we know
It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.
Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Hellyeah, so its true age is longer.
Last checked Nov 4, 2026.
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Upload your resumeWhat the posting discloses
Not stated: Pay, real vacancy, AI screening.
Why these three
Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.
Some employers add pay only on their own site. Check the original posting before you rule it out.
Skills mentioned
About the role
Build the tools that do not exist yet — a campaign simulator, a creative intelligence engine, an experimentation platform. This is invention work, not integration work. Every tool you build becomes a product moat that competitors cannot easily replicate. You will work alongside the intelligence engineer to create a closed-loop optimization system that gets smarter with every dollar spent.
Must Have
Has built platform/tooling systems that unlock new capabilities for users, not just internal plumbing.
- Strong TypeScript or Python with fast production shipping ability.
- Experience with browser automation, screen capture, simulation, or experimentation infrastructure.
- Strong product instincts for turning messy data and workflows into usable tools.
- AI-first development workflow with high implementation velocity.
Nice to have
Simulation engine / Monte Carlo / probabilistic modeling experience
- Computer vision or image analysis (for creative intelligence)
- Ad-tech or growth engineering background
- A/B testing or experimentation infrastructure experience
- Experience with Mastra, Langchain, or LLM orchestration frameworks
Own the platform and simulation layer of Learn Engine.
Build the campaign simulator, experimentation infrastructure, screen-capture and creative-intelligence pipelines, and client-facing data products that let operators model outcomes before spending real money.
This role owns tooling, infrastructure, simulation fidelity, and productized research surfaces — not the optimization policy itself.
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