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
- Pay
- $200k to $1,000k a year USD
Not stated: 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.
Skills mentioned
About the role
Build the brain of an autonomous growth OS. The system you create will manage millions in ad spend and get measurably smarter with every dollar. This is the moat — every competitor has humans optimizing campaigns manually. You are building the intelligence layer that compounds. The Platform engineer creates the tools, you create the decisions. Together you build something nobody else has.
Must Have
Has built recommendation, optimization, or decision systems where outputs improve future inputs.
- Strong statistical reasoning and experimentation judgment under noisy real-world data.
- Strong LLM orchestration or agent-system experience for reasoning over campaign context.
- Can design optimization policies, scoring systems, or automated recommendation loops.
- AI-first development workflow and ability to ship production systems quickly.
Nice to Have
Ad-tech optimization patterns (bid management, budget allocation, ROAS optimization)
- Reinforcement learning (RL) experience is a plus
- Hyperparameter optimization (HPO) experience is a plus
- Model fine-tuning experience is a plus
- Experience building agent-driven automation (LLM agents that take actions)
- Background in growth engineering, performance marketing, or data science
- Experience with Mastra or similar agent orchestration framework
Own the intelligence and optimization layer of Learn Engine.
Build recommendation engines for bid changes, budget reallocation, pause/boost decisions, and postback optimization.
Turn SSOT campaign data into high-quality optimization guidance and closed-loop decision systems.
Define how the system learns from outcomes and continuously improves campaign strategy over time.
This role owns decision quality, optimization policy, and learning loops — not platform plumbing or simulator infrastructure.
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