AI Evaluation Engineer

YOUR ROLE:

At SiteGround, we’ve built and launched our own suite of AI products:

Coderick AI — our vibe coding platform.
AI Studio — access to flagship AI models, providers, and intelligent AI Agents.
WordPress AI Agent — one AI assistant for virtually every WordPress task.

We’re moving fast: adding new capabilities, launching new AI agents, and serving a growing number of customers who use our AI products every day.

This role sits at the heart of how we measure and improve those products. Your job is to make our LLM-powered features provably reliable — not just impressive in a demo.

Prompt engineering is a central part of the role. You’ll design, test, and iterate on prompts and agent behaviour directly. But your defining contribution will be rigour: turning “this seems to work” into measurable, repeatable, production-ready behaviour through evaluation and observability.

We work across the major model ecosystems — OpenAI, Google Gemini, and Anthropic — and you’ll regularly make pragmatic decisions about model choice, quality, latency, and cost.

You’ll work closely with backend engineers, fellow evaluation engineers, and product managers. Together, you’ll dig into real customer interactions, identify where our AI succeeds or fails, and turn those insights into better prompts, agents, tools, and product experiences.

YOUR RESPONSIBILITIES:

**Evaluation & Observability

**

• Build high-quality evaluation datasets with ground truth and define task-specific metrics such as correctness, task completion, efficiency, cost, token usage, and safety;
• Run model bake-offs and before/after prompt experiments — every meaningful prompt change should be a measured experiment, not a guess;
• Design evaluations for harder cases where there is no clean ground truth, including agentic workflows, tool-calling correctness, and structured-output validity;
• Own production observability through trace analysis, per-tool dashboards, error classification, and session-level review;
• Root-cause failures, identify recurring patterns, understand their impact on quality and cost, and turn findings into prompt improvements and engineering tickets;
• Help close the loop between users and product teams by translating real customer behaviour and product questions into clear, measurable insights about how our AI features are used and where they can improve.