About the company
Our client is a fast-growing technology company.
The role
Raydar is recruiting for this role on behalf of our client. Own the decision-making layer of an automated software platform that manages real spend, designing models and policies that improve from past results. You will be accountable for the quality of those decisions, working closely with founders and cross-functional teammates.
What you'll do
- Build recommendation and scoring systems that drive automated adjustments to spend, budgets and campaign status.
- Design feedback mechanisms so the system draws on past results and keeps improving instead of starting over.
- Develop optimization policies for noisy live data, ranging from simple rule-based approaches to post-training and reinforcement learning.
- Design how multiple AI agents reason over shared context, collaborate and have their outputs evaluated.
- Take ownership of decision quality and the financial results it delivers.
Requirements
What we're looking for
- Prior delivery of a production system whose earlier results feed back into its later choices.
- Hands-on use of reinforcement learning, bandits or policy optimization in a live production environment.
- Solid grounding in statistics and A/B testing when working with messy real-world data.
- Working knowledge of advertising optimization concepts such as pacing, budget allocation under caps, and ROAS or CPA targets.
- A quantitative degree in engineering, physics, math, computer science or statistics.
- A self-directed approach, moving projects forward without waiting for instructions.
- Proficiency in Python.
- Ability to work on-site full-time.
Benefits
Compensation and benefits
- Base salary: USD 200,000 to 300,000 per year
- Equity
- Commission structure
Location and work model
- San Francisco, CA, United States
- On-site, 5 days per week in office
- Full-time