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Open nowPosted 58 days ago

AI Engineer — Learn Engine: Intelligence & Optimization

Hellyeah AI11 open roles

Pay
$200,000 – $1,000,000 a year
Where
San Francisco HQ
Work mode
Hybrid
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Your applicationOpen nowAI Engineer — Learn Engine: Intelligence & OptimizationHellyeah AI · San Francisco HQ

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The clock on this job

Early applications get read.

8.2% of postings close within 7 days. Measured by our own scanner across the market. Hellyeah AI postings stay open a median of 1 days.

Share of postings closed within
  1. 1.8%1 day
  2. 3.8%3 days
  3. 8.2%7 days
  4. 15.2%14 days
  5. 34.2%30 days
This job: posted 58 days ago

Hellyeah AI median: 1 days open

The posting

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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