Note: The job is a remote job and is open to candidates in USA. Kake is building a talent pool of Civil Engineering professionals with Python proficiency to contribute to project-based AI development initiatives. This role focuses on evaluating and enhancing frontier AI models using civil engineering simulation tools and requires deep technical problem-solving skills.

Responsibilities

• Identify an appropriate civil engineering simulation package and build problems whose solution genuinely hinges on that tool's core capabilities, whether solvers, simulation kernels, or domain-specific models
• Develop full Python solutions for each problem, providing all necessary input files, geometry definitions, and boundary conditions as required
• Establish the correct numerical output and define how close the AI model needs to get, using tolerance values appropriate to the engineering context
• Run the problem against the AI model across multiple parallel attempts, analyzing where it succeeds or falls short, and adjusting difficulty until the pass rate falls between 10% and 30%
• Tune load cases, boundary conditions, and solver parameters iteratively, building an understanding of how the model navigates complex structural and geotechnical problems
• Hand off completed tasks to a senior reviewer in your subfield and refine based on their feedback before final submission

Skills

• Academic background in Civil Engineering or a closely related field
• At least 2 years of hands-on experience in engineering research, applied work, or teaching
• Solid Python skills, applied to writing and validating computational solutions
• Capacity to build problems that cannot be solved without specialized simulation software
• Excellent written and verbal communication skills in English
• Ability to work independently in a remote, fast-paced environment
• Working knowledge of one or more domain-specific civil engineering tools, including but not limited to OpenSeesPy, CalculiX, YADE, or bempp-cl, or a demonstrated ability to get up to speed independently
• Prior exposure to how frontier AI models approach complex simulation tasks
• Knowledge spanning more than one engineering domain, such as structural analysis, geotechnical modeling, or boundary element methods
• Familiarity with containerized or sandboxed Linux execution environments

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