# AI Engineer — Learn Engine: Intelligence & Optimization at Hellyeah

> Hybrid · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Ahellyeah%3A670fd123-0331-46da-b973-e46fe64a48e4 (read 2026-11-08).

## Facts (observed)

- Company: [Hellyeah](https://alderhq.app/companies/hellyeah)
- Location text: San Francisco HQ; Shanghai
- Where it can be done from (inferred): Hybrid · United States (evidence: "Location: San Francisco HQ; Shanghai")
- Posted: 2026-08-12 (88 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/hellyeah/670fd123-0331-46da-b973-e46fe64a48e4
- Skills listed (inferred): LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 461): p25 $200k, median $250k, p75 $300k

## Description (the employer's text)

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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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Ahellyeah%3A670fd123-0331-46da-b973-e46fe64a48e4 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.