About the role
We need a founding engineer who can own the AI systems at the core of our product. Not someone to slot into an existing stack — someone to define it.
Here's a concrete example of the kind of problem you'd solve:
A vehicle arrives at a shop with a check-engine light and a P0420 catalytic converter efficiency code. The system needs to pull that vehicle's full repair history, cross-reference common failure patterns for the make/model/mileage, retrieve current parts pricing from multiple suppliers, estimate labor time based on the shop's historical data, and surface a recommended repair plan to the service advisor — grounded, priced, and ready to present to the customer. All in seconds, and reliably enough that 500 shops can trust it every day.
That touches data ingestion, retrieval, grounding, agent orchestration, and quality measurement. That's the job.
What you'll own
- Agent orchestration & estimate generation — Design and improve the multi-agent workflows that transform a vehicle's symptoms, history, and parts availability into grounded, accurate estimates. You'll work across retrieval, context assembly, and generation.
- Data & retrieval infrastructure — Build and maintain the pipelines that ingest repair-order data from shop management systems, normalize messy real-world automotive data, and make it retrievable for downstream AI systems.
- Evals, benchmarks & quality loops — Build the evaluation frameworks that measure estimate accuracy, grounding quality, and recommendation reliability. Estimate consistency is the core product promise — you'll own the systems that prove it works and catch when it doesn't.
- Product delivery — Ship user-facing features end-to-end and work directly with founders and early customers to iterate on what advisors actually need.
- Technical direction — Help define the product roadmap, make architectural decisions, and shape engineering culture. As we grow, help recruit and mentor the engineering team.