Founding AI Engineer

Mondrio•San Francisco, CA

2d•$225,000 - $255,000•Remote

About The Position

Mondrio's AI recommends prices, and expert Pricing Architects stay in the loop on the high-stakes calls. Your mandate is to build the evaluation systems and feedback loops that let the AI earn more of that trust. This is applied AI on a problem where quality is measurable in customer revenue.

Requirements

8+ years of engineering experience with strong and recent production LLM depth.
You have shipped LLM-powered product features to production and owned them after launch.
You have built evals and observability for LLM systems yourself. Running someone else's dashboard does not count.
Strong communication skills to bridge the technical gap around non-deterministic engineering to less savvy clients and partners
Product engineer instincts: you pick your own scope and choose the pragmatic option over the interesting one. This is not a research-lab role.
You can show how AI coding tools fit into your work today. We weigh that over where you studied or previous role.
Work authorization: You must be authorized to work in the US. We're unable to sponsor visas at this time.

Nice To Haves

Experience with MCP or building tools for LLM agents.
Experience with platforms such as LangChain, LlamaIndex, Braintrust, OpenRouter
Work in a domain where correctness is audited, such as pricing, billing, or payments.
Familiarity with data residency or compliance constraints. SOC2 and GDPR shape what you build against.

Responsibilities

Build evaluation for AI pricing recommendations: eval harnesses and benchmarks that use tracked pricing outcomes as ground truth. Expert review is manual today, and you make it systematic.
Take AI personas further. They simulate B2B buying committees and behavioral effects such as new versus existing customers, grounded in usage data and call transcripts. Automate the parts of persona training that are still manual.
Own LLM infrastructure: routing across current (Anthropic and Google) and future models, with explicit cost, latency, and quality tradeoffs.
Maintain infra and data residency boundaries (e.g. model calls for EU customers must remain within the EU) as we add providers and scale up operations.
Extend the MCP server that LLM agents, including our customers' own agents, use to drive the platform. A feature is done when an agent can drive it through MCP, not when the React component renders.
Work within our typed ontology of pricing entities (Pydantic models for SKU, Proposition, Persona, Quote) so model outputs land in structured, auditable form.
Conduct code reviews and lead a technical session on advanced AI/ML patterns for the team.
Participate actively in interview loops to scale the engineering team and mentor mid-level engineers.