Applied AI Engineer

The role asks for someone to own an evaluation system that feeds a self improving agent loop, and to push the quality of AI generated design output. A strong candidate has hands on experience with AI models or agents, plus building evals, post training, or context engineering. They should think from first principles and work with product to turn user feedback into agent improvements. How it fits the AI era AI is central here: the product itself turns prompts and screenshots into UI components, so the engineer works directly on model behavior and agent quality. Much of the craft is in measuring and steering the agent, which still needs human judgment about what good design looks like. The role stays human in deciding what problems to tackle and interpreting user needs. How it builds AI experience Working on evals, post training, and context engineering for a design focused agent gives direct experience with model improvement loops. Building a self improving agent that serves thousands of users is a rare chance to see how evaluation drives real product quality. That experience transfers to any team shipping AI features. Worth showing Eval system building Post training experience First principles thinking This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role. About Magic Patterns AI design platform that converts natural language prompts and screenshots into production-ready UI components, per the company.

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