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Experts working on micro1 pipelines are welcome to include this experience on their LinkedIn profiles. However, because many pipelines involve confidential client work, there are important guidelines that must be followed. The key principle is simple: You can describe the type of work and domain, but you should not reveal any client, pipeline or internal information. These guidelines help protect confidentiality while still allowing experts to showcase their contributions to AI development.

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What you can mention

Experts may describe their work at a high level, focusing on the domain expertise they bring to AI training pipelines.
  • Acceptable information includes:
    • The fact that you are working on AI training or model development pipelines
    • Your domain expertise (e.g., finance, mechanical engineering, medicine, law)
    • The type of tasks performed (annotation, evaluation, reasoning tasks, expert review, etc.)
    • The broader goal of the work (helping train AI systems)
  • Examples of acceptable descriptions:
    • Helping train AI models using domain expertise in mechanical engineering
    • Contributing expert knowledge to improve AI model reasoning in the finance domain
    • Working on AI training pipelines focused on improving technical accuracy in legal reasoning
    • Helping train large language models using subject-matter expertise in medicine
    • Supporting the development of next-generation AI systems through expert data annotation and evaluation

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What should not be mentioned

Experts should never include confidential or identifying information related to the pipelines or client.
  • The following should not be shared on LinkedIn:
    • Client names
    • Pipeline names
    • Internal pipeline code names
    • Detailed descriptions of the task workflow
    • Screenshots of tools or interfaces
    • Specific datasets or prompts used
    • Internal instructions or evaluation criteria
    • Information about where the work was conducted
  • Examples of what not to write:
    • “Training models for XXX company”
    • “Working on the XXX pipeline”
    • “Evaluating prompts used in [client name]’s internal AI system”
    • “Labeling training data for [specific product or model]”
Even if the information seems harmless, it may violate NDAs or client confidentiality agreements.

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Example job titles experts commonly use

Experts can choose titles that reflect their work while remaining general. Common examples include:
  • AI training expert
  • Human data expert
  • Domain expert in ___

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Example LinkedIn experience entries

  • ​​Example 1
    • Title: Finance Expert
    • Company: micro1
    • Description: Contributing financial domain expertise to help train and evaluate large language models. Work involves reviewing model responses, improving reasoning quality, and supporting the development of AI systems used across industry applications.
  • Example 2
    • Title: Human Data Expert
    • Company: micro1
    • Description: Applying subject matter expertise to support AI training pipelines. Tasks include evaluating model outputs, reviewing technical reasoning, and contributing domain knowledge used to improve model performance.
  • Example 3
    • Title: AI Voice Expert 
    • Company: micro1
    • Description: Supporting the development of advanced AI systems by contributing expert feedback and domain-specific evaluation of model responses.
  • Example 4 
    • Title: AI training expert
    • Company: micro1
    • Description: Helping train large language models by applying domain expertise in economics and finance.