Clera Founding AI Engineer at Clera responsible for building evaluation systems, feedback loops, and LLM infrastructure for an AI-powered pricing platform. Owns core AI infrastructure connecting model outputs to business outcomes.
Responsibilities
This is a founding-level AI Engineering role at an early-stage B2B SaaS startup building an AI-powered pricing platform. You'll join a small, product-focused team and own the evaluation systems, feedback loops, and LLM infrastructure that connect model outputs directly to revenue and compliance outcomes. This is a high-stakes, high-ownership position — your work will sit at the core of how pricing AI earns trust and drives measurable business impact for B2B customers.
You'll be one of the first engineers on the team, shaping technical direction in a lean environment where ambiguity is the norm and shipping to customers is the measure of success.
Build eval harnesses and benchmarks that use tracked pricing outcomes as ground truth for model improvement.
Systematize and automate expert review workflows currently performed manually.
Develop AI personas that simulate B2B buying committees and behavioral effects using usage data and call transcripts.
Automate persona training pipelines that are today manual processes.
Own LLM routing across providers (e.g., Anthropic, Google) with explicit cost, latency, and quality trade-offs.
Maintain infrastructure and data residency boundaries — for example, ensuring EU model calls remain within the EU.
Extend the MCP server used by LLM agents (including customer-facing agents) so that product features are agent-driven.
Define "done" as when agents can drive features through MCP, not just when a UI renders them.
Work within a typed ontology of pricing entities (e.g., SKU, Proposition, Persona, Quote via Pydantic models) so model outputs are structured and auditable.
Identify and remediate systemic latency, data drift, and cold-start issues in the pricing loop.
Qualification
Background in domains where pricing
Required
8+ years of engineering experience with strong, recent, production LLM depth — including shipping LLM-powered product features to production and owning them post-launch.
Proven experience building eval harnesses and observability for LLM systems — not just dashboards, but writing the evals and baselining prompts against typed ontologies to support release gating.
Experience building evaluation benchmarks for AI recommendations in a revenue-impact domain (pricing, billing, payments, or similar).
Hands-on experience with LLM infrastructure: routing across multiple models, managing cost/latency/quality trade-offs.
Experience working with structured data models and typed ontologies to ensure model outputs are auditable.
Strong communication skills — able to explain non-deterministic systems clearly to clients, pricing experts, and non-technical stakeholders.
Comfortable operating in a lean startup environment with high ownership and rapidly shifting priorities.
Must be authorized to work in the United States. No visa sponsorship is available at this time.
Preferred
Experience with MCP or building tools/integrations for LLM agents.
Familiarity with platforms such as LangChain, LlamaIndex, Braintrust, or OpenRouter.
Background in domains where pricing, billing, or payment accuracy and auditability are required.
Experience navigating data residency or compliance constraints (SOC2, GDPR) and implementing related controls.
