@seclink: Fun fact: In the current field of large model evaluation, there is high demand, high salary, and scarce talent. The following directions may have daily no-fault salaries of 30k - 80k: 0. Systematically identifying core weaknesses of models in scenarios like finance, healthcare, mini-programs/APPs, and pushing forward a technical closed loop of "evaluation-feedback-optimization". Such talents are very scarce. Some...

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Summary

The article points out that in the field of large model evaluation, there are positions with high demand, high salary, and scarce talent, especially in systematically identifying model weaknesses, specialized expert systems, and multi-step agent data evaluation. Daily salaries for related talents can reach 30k-80k.

Fun fact: In the current field of large model evaluation, there is high demand, high salary, and scarce talent. The following directions may have daily no-fault salaries of 30k - 80k: 0. Systematically identifying core weaknesses of models in scenarios like finance, healthcare, mini-programs/APPs, and pushing forward a technical closed loop of "evaluation-feedback-optimization". Such talents are very scarce. Some master's graduates can negotiate an annual salary of 1 million within three years of starting work. 1. Specialized expert systems (essentially annotation, distilling expert capabilities into large models; detailed domain data is still scarce) - For example, in high-precision fields like medicine, chemical engineering, and new materials, data annotation is still needed. - For example, in the field of network security, penetration testing, data annotation for training security large models (annotating niche programming languages, annotating SDK usage from major companies/banks [documents are scarce but the SDKs are influential] (e.g., various custom SDKs of Alibaba Cloud Open Platform, various SDK usage of TOP platform, many large models cannot generate stable code on the CLI side due to lack of data. Data is needed to enable large models to compile successfully in one go, avoiding the token loss from repeated debugging). 2. Multi-step agent data evaluation: - For example, supporting annotation and evaluation optimization of multi-step behavioral trajectories, training data organization. - For example, supporting multimedia data optimization for video understanding, such as data annotation in images and videos.
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Cached at: 07/15/26, 03:42 AM

Trivia:

In the current landscape of large model evaluation, there is high demand, high pay, and a scarcity of talent. The following directions may offer daily uncompensated wages of 30k–80k:

  1. System positioning models that identify core shortcomings in finance, healthcare, mini-programs/apps, etc., and drive a “evaluation-feedback-optimization” technical closed loop. Talents like these are rare. Some master’s graduates can reach an annual salary of 1 million within three years of starting work.

  2. Domain-specific expert systems (essentially labeling, embedding expert capabilities into large models; domain-specific data is still scarce)

    • For example, in high-precision fields like pharmaceuticals, chemicals, advanced materials, data annotation is still needed.
    • For example, in cybersecurity and penetration testing, data annotation for training security large models (labeling niche programming languages, labeling SDK usages from major companies/banks [documents are scarce, but the SDKs are highly influential], e.g., various custom SDKs from Alibaba Cloud’s open platform, various SDK usages from TOP platform; many large models cannot generate stable code on the CLI side due to lack of data. Data is needed to enable large models to compile successfully on the first attempt, avoiding token waste from repeated debugging.)

  3. Agent multi-step data evaluation:

    • For example, supporting multi-step behavior trajectory annotation, evaluation, optimization, and training data organization.
    • For example, supporting multimedia data optimization for video understanding, such as data annotation within image information and video information.

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