AI Quality Assurance Intern

About Neto

Neto is an agentic AI platform built for the legal industry, helping law firms capture and convert more leads through voice, SMS, chat, and email. Our AI agents act as virtual receptionists and intake specialists, driving faster response times and higher client conversion rates. Today, Neto supports over 200 law firms nationwide.

Position Overview

We are seeking a detail-oriented AI Quality Assurance Intern to support the optimization and performance of our AI agents. This role is ideal for someone interested in AI, language models, and real-world application of automation in high-stakes environments like legal intake.

Key Responsibilities

* Review AI-generated phone call transcripts and text conversations for accuracy, tone, and effectiveness

* Identify errors, inconsistencies, or missed opportunities in AI interactions

* Help design and refine prompts for autonomous review agents that evaluate AI performance

* Collaborate with product and engineering teams to improve agent behavior and outcomes

* Document findings and contribute to continuous improvement of Neto’s AI systems

Qualifications

* Strong written and verbal communication skills

* High attention to detail and critical thinking ability

* Interest in AI, automation, and conversational design

* Ability to analyze conversations and identify patterns or issues

* Comfortable working with data, transcripts, and structured feedback systems

Preferred (Not Required)

* Experience with prompt engineering or AI tools (ChatGPT, etc.)

* Background or interest in legal, customer service, or sales processes

Compensation

$15.15 per hour

What You’ll Gain

* Hands-on experience with cutting-edge AI systems in a real-world environment

* Exposure to prompt engineering and AI quality assurance workflows

* Opportunity to directly impact product performance and customer outcomes

* Mentorship from experienced operators in AI and SaaS

Location & Duration

Scottsdale, AZ (Hybrid or Remote flexibility available)

Duration and hours to be determined based on candidate availability