Product Analyst, AI Quality

Clear Capital | CubiCasa

4d•$81,000 - $105,600•Hybrid

About The Position

The Product Analyst, AI Quality is the operational bridge between product team’s intent and the Machine Learning (ML) team's day-to-day execution on our real estate photo-editing pipeline. This person translates product requirements into ML-actionable specs, manages the data acquisition and labeling roadmap, maintains the exemplar standard for what "good" output looks like, and runs evaluation of model outputs so the ML team always has a resolved, forward-looking queue of work. This role will also provide helpful research and analysis to the overall CubiCasa Photo Editing product set as needed.

Requirements

1–5 years of experience in a product analyst, technical PM, or related operational role with exposure to data/ML.
Background in real estate photography, commercial photo editing, or visual media workflows.
Comfort evaluating structured data against defined standards and documenting reasoning clearly.
Strong written communication for writing specs and guidelines.
Organized and proactive; comfortable running planning cadences.
Familiarity with basic ML/data concepts (labeling, model evaluation).
Experience coordinating with operations/production teams on review-at-scale work.

Responsibilities

Run evaluation of model outputs and training runs against agreed criteria, and make go/no-go recommendations.
Convert product requirements (quality targets, feature specs, editing behavior expectations) into concrete, ML-team-actionable specs — training objectives, acceptance criteria, edge cases, and failure modes.
Help scope datasets, write labeling guidelines, and define data quality standards alongside the ML team.
Plan data acquisition — what data is needed, from where, in what volume, and at what cadence — ahead of the ML team’s need whenever possible.
Procures and maintains a definitive library of exemplar outputs for AI assisted features and keeps this library current.
Coordinate resources involved in assessments at scale and track regressions.
Run planning with the ML team and serve as the first line of product-question resolution.