ParaView Specialist - Remote AI Research

What You'll Do8

• Create domain prompts that test how well AI models understand ParaView workflows and computational science concepts.
• Review LLM answers for scientific visualization, fluid dynamics simulation, and large dataset analysis tasks.
• Write feedback that pinpoints where model output falls short and how the answer should change.
• Use hands-on ParaView knowledge to judge whether AI-generated steps match real visualization or HPC practice.
• Compare model responses against accepted methods for processing and displaying large datasets.
• Flag wrong assumptions about simulation setup, rendering choices, or data pipelines.
• Build examples that show correct model behavior next to common errors.
• Work with AI research teams on a schedule you set, with project placement based on available work.

Requirements8

• At least one year of hands-on work with ParaView.
• Experience using related technical tools for scientific visualization, simulation, or data analysis.
• Background in fluid dynamics simulation, large dataset analysis, or a similar computational field.
• Ability to review AI output with care and explain technical errors in plain language.
• Skill in writing prompts or evaluation criteria for LLM responses.
• Capacity to work on an asynchronous schedule with AI research teams.
• Familiarity with HPC workflows and computational science methods.
• Comfort handling project work without set hours or direct supervision.

Who Should Apply

The ideal candidate has at least a year of real ParaView use, plus exposure to fluid dynamics simulation, scientific visualization, or large dataset analysis. You should feel comfortable judging AI answers when they describe computational science steps or HPC workflows, and you need a work style that fits an asynchronous, project-based setup. People who want a fixed schedule, steady long-term tasks, or close daily direction will find this role a poor match. Applicants often score low when they list ParaView as a minor tool with no concrete project examples or cannot explain how they would check an LLM response for technical accuracy. Another common gap is failing to show 1+ years of hands-on work, since the project needs contributors who know the tool from actual use.

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