AI Engineer — Learn Engine: Intelligence & Optimization
Ai ml · Posted 13 days ago
You own the intelligence layer of Learn Engine, the part of Hellyeah's autonomous growth OS that decides what happens to a campaign. The platform engineer builds the tools; you build the decisions. The system manages real ad spend, so every decision you design is measured against returns.
- Way of working
- On site
- Location
- San Francisco, CA
- Pay range
- $200,000 to $300,000
- Level
- Mid
- Experience
- 3+ years
- Type
- Full time
- Visa sponsorship
- Not offered for this role
- The company
- 20 to 50 people
Skills that matter here
What you would be doing
- Build the decision engine. Recommendation and scoring systems for bid changes, budget reallocation, pause and boost calls, and postback optimization.
- Design the learning loop. Define how the system learns from outcomes so campaign strategy improves over time rather than resetting.
- Write the policies. Optimization policies over noisy live data, from simple rule-based strategies through post-training and RL.
- Orchestrate agents. Design how multiple agents reason over campaign context and work together, and evaluate what they produce.
- Own the number. You are accountable for decision quality and for what the spend returns, not for platform plumbing or simulator infrastructure.
The full description
You own the intelligence layer of Learn Engine, the part of Hellyeah's autonomous growth OS that decides what happens to a campaign. The platform engineer builds the tools; you build the decisions. The system manages real ad spend, so every decision you design is measured against returns.
You will feel at home here if you are the kind of engineer who is already tinkering with something new this week.
What You'll Do
- Build the decision engine. Recommendation and scoring systems for bid changes, budget reallocation, pause and boost calls, and postback optimization.
- Design the learning loop. Define how the system learns from outcomes so campaign strategy improves over time rather than resetting.
- Write the policies. Optimization policies over noisy live data, from simple rule-based strategies through post-training and RL.
- Orchestrate agents. Design how multiple agents reason over campaign context and work together, and evaluate what they produce.
- Own the number. You are accountable for decision quality and for what the spend returns, not for platform plumbing or simulator infrastructure.
Interested in this one?
There is no apply button here on purpose. Tell us about yourself, we book a short call, and if this role fits we walk you through the company and ask before anything is sent. Always free for you.
Tell us about you