Samsung Food (Whisk)

backend developer for AI coaching

In short

A 13-week lead-level remote engagement to build an evaluation pipeline for an AI coaching product within Samsung Food (Whisk): failure taxonomy, grading rubrics, LLM judge validation, and handover to an internal owner. It demands hands-on experience running a full evaluation loop on conversational/generated-text output, including building failure taxonomies and validating LLM judges against human grades. No production coding or domain nutrition expertise required. Fits someone experienced in AI product quality, model evaluation, or qualitative data annotation rather than traditional backend engineering.

About
Samsung Food connects food, health, and home across the Samsung ecosystem, turning device data into personalized guidance. Its app offers connected, proactive, and personalized experiences, including Vision AI for recognizing food and meals and integrations with Samsung Health and SmartThings.
Tasks
•Build a failure taxonomy and golden set by hand-grading traces, including sparse-data synthetic profiles, documenting failure categories and their frequency, demonstrating saturation, and freezing holdout traces
•Create a binary pass/fail rubric for each taxonomy category, with definitions and examples, and write annotation guidelines
•Double-code traces with a second grader, log and resolve disagreements, and maintain a guideline revision log
•Create judge prompts for each criterion and validate the judge against human grades on development and holdout sets
•Run weekly readouts on helpfulness, relevance, and tone, including a run by the internal owner with the consultant shadowing
•Document a revalidation routine triggered by every model or prompt change and run it quarterly
•Write a playbook covering grading, taxonomy updates, rubric revisions, judge revalidation, and readouts
•Handover the process and test that the internal owner can independently rerun judge validation and a weekly readout
Requirements
•Have run the end-to-end evaluation loop at least once on conversational or generated-text output: trace error analysis, a self-developed failure taxonomy, binary criteria and annotation guidelines, and an LLM judge validated against personal labels
•Be able to describe a personally discovered failure mode that had not previously been identified
•Be comfortable acting as the arbiter and documenting decisions and reasons for overruling
•Understand that the rubric is discovered through grading rather than written upfront
•Be able to explain why a judge that agrees 94% of the time may still be useless
•Write annotation guidelines that require no interpretation
•Be comfortable working in notebooks and spreadsheets; production coding is not required
•Nutrition, weight management, and behavior-change expertise are not required; the company has this expertise in-house
Nice to have
Conversation design, AI product quality, model behavior or policy, content design, human data operations, UX research with strong qualitative coding, RLHF, applied linguistics, product management
Conditions
The engagement is an approximately 13-week project

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