AI公司招募各行业专家训练和评估AI系统

An AI system can produce a convincing explanation of a clinical trial. It can generate a set of engineering calculations. It can even transcribe a conversation between an aircraft and an air traffic controller. But how do you know whether any of it is right? In these opportunities, AI companies turn to people who already know the answer. Job and project descriptions from specialist AI training providers reveal an unusual category of work: recruiting experienced professionals to create difficult problems, verify solutions, annotate specialist data and evaluate AI systems against the standards of their professions. A biostatistician is asked to construct clinical-trial evaluation tasks and define detailed grading criteria. An air traffic controller is recruited to interpret real aviation radio transmissions. An engineer is asked to test whether an AI agent can complete work inside professional CAD software. The professionals are not necessarily being recruited to build AI. They are being recruited because they understand the work AI is expected to perform. Where do your skills stand in today's job market? Glassbox compares the skills on your CV with the skills employers are requesting, so you can see where they overlap. Your CV is probably on your laptop. Comparing it there takes a minute. Copy linkSend to yourself

1. Before AI can be judged, someone has to define the right answer Consider the work requested of a micro1 Biostatistician. The opportunity asks for specialists who can create evaluation tasks based on clinical-trial analysis, regulatory submissions and observational studies. Contributors are expected to construct or source datasets, define appropriate methodologies, provide defensible interpretations and develop detailed rubrics for evaluating AI agents. The description calls for 35+ item rubrics. That detail matters. Evaluating a statistical answer is not simply a matter of checking whether a final number matches a reference. The method, assumptions, interpretation and clinical implications can all affect whether the result is defensible. A separate Business Operations Expert opportunity follows a similar idea outside science. Experienced practitioners are asked to design realistic, multi-step business tasks, supply supporting materials and define objective grading criteria. In both cases, the specialist is helping establish what competent work looks like. Sources: Biostatistician · Business Operations Expert One of the clearest examples comes from aviation. A micro1 Air Traffic Controller opportunity asks current or former certified controllers to listen to authentic air traffic control radio transmissions and produce accurate, structured transcripts. The work involves identifying headings, altitudes, frequencies and clearances. Contributors are also expected to use contextual resources such as airport diagrams and approach charts to resolve ambiguous or noisy audio. The distinction is important: a transcription system might recognise the words in a transmission, but interpreting what those words mean requires familiarity with aviation communications and operational context. The listing specifies a demanding transcription accuracy target, including a stated word-error-rate threshold of 2% or lower. Other specialist opportunities ask people with agricultural, forestry and environmental experience to review datasets and scenarios using their technical and regulatory knowledge. In manufacturing, an Invisible Technologies Garment Manufacturing Quality Control project asks experienced practitioners to assess factory quality scenarios, defect classifications and model reasoning. These examples point to a different use of expertise: supplying the context that turns raw information into meaningful, reliable data. ILLUSTRATIVE RECONSTRUCTION · NOT SOURCE AUDIO 02 / EXPERT CONTEXTCharts + operational knowledgeResolve ambiguous or noisy segments 03 / EVALUATIONStructured transcriptAccuracy target: ≤2% word error rate “Identify and extract critical instructions—including headings, altitudes, frequencies, and clearances.” “Apply your operational experience to clarify ambiguous or noisy audio segments.” Illustrative process diagram, not a transcription or source artifact. Excerpts are from the linked micro1 opportunity; the listing gives a target of 2% word error rate or lower. Sources: Air Traffic Controller · Agriculture, Forestry and Environmental Experts · Invisible Technologies — Garment Manufacturing QC These engineering projects ask a different question. Mindrift's specialist engineering projects involve testing AI agents inside the software environments engineers actually use. Its CAD Engineer opportunity asks contributors to design tasks, create reference solutions and evaluate agents operating in CAD software. Its Computational Fluid Dynamics project involves setting up simulations, producing verified reference solutions and creating Python checks to assess an agent's work. The point is not simply whether an AI model can explain how a simulation works. It is whether an agent can execute the task, use the tools correctly and produce a result that an experienced engineer would accept. A chatbot answer can be compared with a reference response. A professional workflow may require checking multiple steps, intermediate outputs, tool interactions and the final result. The expert is therefore evaluating more than language. They are evaluating work. 1234501Professional knowledge What counts as competent work? Sources: Mindrift — CAD Engineer · Mindrift — CFD Engineer The opportunities reviewed span disciplines with very different definitions of correctness. Biology & chemistryProblems, experimental setups and verified scientific answers. BiostatisticsMethods, assumptions, interpretation and defensible rubrics. AviationOperational context for headings, altitudes, frequencies and clearances. LawDocument review and profession-specific reasoning standards.

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