E-commerce Simulation Expert

Job Summary

The E-commerce Simulation Expert designs, builds, and evaluates complex synthetic retail environments to train frontier AI models and autonomous agents. Core daily duties include engineering realistic digital product catalogs and category layouts; developing authentic consumer persona scenarios and edge-case prompt vectors; constructing strict quality assurance grading rubrics to test model reasoning; identifying system failure patterns; and collaborating with data science teams to optimize training data quality.

Foundational Capabilities & Prerequisites

• Professional Footprint: Minimum of 3 years of hands-on experience operating directly within core e-commerce operations, digital merchandising, or product catalog management roles.
• Technical Architecture Depth: Proven expertise managing product catalog structuring, data normalization rules, and high-quality content creation pipelines for digital storefronts.
• Cognitive Skills: Strong analytical thinking paired with a rigorous quality assurance (QA) mindset, exhibiting meticulous attention to detail during scenario and data generation.
• Consumer Behavior Literacy: Demonstrated operational understanding of consumer behavior dynamics, purchasing intent variables, and modern online shopping journeys.
• Articulation Skills: Exceptional written and verbal communication skills, showing a clear ability to describe complex concepts effectively to technical teams.
• Scalable Autonomy: Proven capability to operate completely independently and deliver high-quality, scalable datasets within fast-moving project environments.

Preferred Technical Multipliers

• AI Alignment Exposure: Past experience directly training AI models, managing LLM prompt engineering tracks, or working inside specialized simulation and data design frameworks.
• Advanced Industry Sight: Deep knowledge of emerging technology trends inside e-commerce systems, retail analytics, or automated digital merchandising.
• Methodological Depth: Familiarity with Large Language Model (LLM) evaluation frameworks, prompt structures, and iterative data improvement methodologies.

Technical Workflow Architecture

As an E-commerce Simulation Expert at micro1, your daily contributions will balance e-commerce structures with machine learning data design:

• Synthetic Store Prototyping: Constructing extensive, complex product category models featuring intentional data anomalies, missing attributes, and pricing variances to test if AI agents can navigate broken retail databases.
• Adversarial User Simulation: Writing highly specific customer prompts that mimic complex real-world behaviors—such as indecisive shoppers, contradictory preferences, or ambiguous refund claims—to see if the AI model drops context or loses reasoning logic.
• Prompt Infrastructure & Evaluation: Designing dual-track prompt payloads (system instructions vs. user inquiries) and testing them across frontier LLMs to document precisely where model logic fails.
• Performance Failure Diagnostics: Reviewing automated chat transcripts generated by AI shopping assistants, identifying structural patterns in their errors, and adjusting the training data to fix those specific weaknesses.

Key Responsibilities

1. Synthetic Store Architecture & Catalog Generation

• Design and build realistic, synthetic digital store environments containing complex product assortments, multi-tiered pricing, and logical category structures.
• Generate, normalize, and maintain comprehensive mock product catalogs, ensuring total data quality, consistency, and real-world behavioral complexity.
• Structure rich item attribute metadata (e.g., sizing matrix variants, compliance rules, tax tags) to build realistic sandboxes for AI agents.

2. Scenario Design & Consumer Simulation

• Develop diverse, authentic online shopping scenarios and user prompts that accurately simulate varied consumer preferences, purchasing paths, and search intents.
• Engineer challenging edge-case situations, including complex order changes, matching conflicting item descriptions, or navigating vague customer requests.
• Translate real-world customer service friction points into structured datasets designed to optimize AI conversational capabilities.

3. Rigorous Evaluation, QA & Rubric Construction

• Create detailed, objective evaluation rubrics and conduct rigorous quality assurance testing to systematically assess AI-generated responses for mathematical accuracy, conversational relevance, and underlying reasoning logic.
• Monitor model performance outputs continuously, isolate systemic failure patterns, and refine training data inputs to drive iterative, step-by-step improvements.
• Document workflows, share data insights, and help establish operational best practices for simulation-based AI training across micro1.

4. Cross-Functional Data Science Collaboration

• Collaborate directly with AI researchers and data science engineering teams to translate your practical e-commerce expertise into actionable training datasets and software insights.
• Bridge the structural gap between retail operational rules and automated algorithmic systems by converting your domain knowledge into precise logic guidelines.

Core Competencies & Analytical Strengths

• Merchandising Logic: Applying deep taxonomy knowledge to spot listing mistakes, incorrect parent-child variations, and data inconsistencies in automated scripts.
• Consumer Persona Simulation: Stepping into the shoes of varied online consumer archetypes to generate realistic prompts that expose model logic gaps.
• Analytical Diagnostics: Systematically sorting through hundreds of model outputs to diagnose exactly where and why an AI tool miscalculated pricing or ignored filter rules.
• Asynchronous Data Scale: Designing organized, clean, and massive data templates that data scientists can easily feed directly into model fine-tuning runs.

Skills Required:

• Computer / Software / It / Data