Founding AI Engineer

Role overview

Responsibilities

• check_circle Design and build eval harnesses and benchmarks that use tracked pricing outcomes as ground truth, supporting release gating against a typed ontology of pricing entities.
• check_circle Systematize and automate expert review workflows currently performed manually.
• check_circle Develop AI personas that simulate B2B buying committees and behavioral effects, leveraging usage data and call transcripts.
• check_circle Automate persona training pipelines that are today manual processes.
• check_circle Own LLM routing across providers (e.g., Anthropic, Google) with explicit cost, latency, and quality tradeoffs.
• check_circle Maintain infrastructure and data residency boundaries (e.g., ensuring EU model calls remain within the EU; SOC2/GDPR compliance).
• check_circle Extend the MCP server used by LLM agents — including customer-facing agents — so that features are agent-driven, not just UI-rendered.
• check_circle Work within a typed ontology of pricing entities (e.g., Pydantic models for SKU, Proposition, Persona, Quote) so model outputs are structured and auditable.
• check_circle Identify and remediate systemic latency, data drift, and cold-start issues in the pricing loop.
• check_circle 8+ years of engineering experience with strong, recent production LLM depth — including shipping and owning LLM-powered features end-to-end after launch.
• check_circle Must be authorized to work in the United States; visa sponsorship is not available.
• check_circle Hands-on experience building evals and observability for LLM systems — creating eval harnesses, baselining prompts, and supporting release gating.
• check_circle Experience owning LLM infrastructure, including routing across multiple model providers with cost, latency, and quality tradeoffs.
• check_circle Proficiency with structured data models and typed ontologies (e.g., Pydantic) to ensure model outputs are structured and auditable.
• check_circle Full end-to-end ownership of LLM-powered features — from development through production monitoring.
• check_circle Strong communication skills to explain non-deterministic systems to non-technical clients and stakeholders.
• check_circle Product-engineer instincts: ability to scope and deliver pragmatic solutions in a fast-moving environment.

Preferred qualifications

• Experience with MCP or building tools/integrations for LLM agents.
• Familiarity with platforms such as LangChain, LlamaIndex, Braintrust, or OpenRouter.
• Experience in domains where pricing, billing, or payments accuracy and auditability are required.
• Familiarity with data residency or compliance controls (SOC2, GDPR).

Benefits

• check_circle Salary: $225,000 – $255,000 USD annually
• check_circle Founding-team equity opportunity at an early-stage, high-growth startup