The work
You will apply practical sales, revenue, and customer success operations judgment to realistic scenarios used in AI training. You will explain your decisions clearly, identify unclear requirements, and avoid making unsupported assumptions.
- Evaluate CRM configuration choices involving fields, workflows, automation, and validation rules.
- Assess workflow designs and integration tradeoffs across CRM, revenue, and customer success tools.
- Resolve conflicts between operational policies by deciding which rules take precedence and what should stay unchanged.
- Diagnose duplicate records, mismatched numbers, and inconsistent definitions across connected systems.
- Explain how to find the root cause of data-quality issues.
- Describe what helps sales and customer success teams adopt workflows, adapt them, or work around them.
- Flag scenarios that do not provide enough information for a sound decision.
What it pays and takes
This is a remote, part-time contractor opportunity for professionals who can work in English and are based in the United States or Canada.
- Pay: $40 to $85 per hour.
- Location: Open to candidates in the United States or Canada.
- Language: Fluent in English.
- Experience: At least four years of hands-on sales, revenue, or customer success operations experience in SaaS.
- CRM experience: Personal responsibility for configuring and maintaining Salesforce, HubSpot, or a similar CRM.
- Connected systems: Experience with at least two tools outside the CRM, such as quoting, billing, ticketing, or project tracking systems.
- Communication: Strong operational judgment and the ability to explain decisions to technical and non-technical audiences.
- Helpful background: Experience maintaining connected revenue operations systems and diagnosing data-quality issues.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is where you find this role and start the application; it does not employ or pay you for the work.
About AI training work
AI training is the human work behind artificial intelligence. Contributors review examples, evaluate outputs, and explain decisions so AI systems can improve, while people with practical professional expertise help make those evaluations accurate and useful.