Civil Engineer & Python Expert – Freelance AI Trainer

Remote from
UK flag
UK
Salary, yearly, USD
72,000
Employment type
Full Time,
Job posted
Apply before
8 Jul 2026
Experience level
Senior
Views / Applies
18 / 5

About Mindrift

Mindrift connects AI experts and clients to advance Generative AI models.

Actively Hiring
Verified job posting
This job post has been manually reviewed for authenticity and compliance.

AI Summary

This freelance role involves designing computational engineering problems that challenge frontier AI models, requiring specialized tools like OpenSeesPy, CalculiX, or YADE. As a Civil Engineer and Python Expert, you'll write reference solutions, tune problem difficulty, and collaborate with senior reviewers. The position is project-based with an estimated 10-20 hours per week, offering up to $36 per hour. Ideal candidates have a civil engineering background, Python proficiency, and a willingness to learn new tools. This opportunity provides hands-on experience with AI systems and deepens expertise in structural and geotechnical simulations.

Role DNA

Job Complexity
Easy Hard
Pace & Pressure
Relaxed Fast-paced
Autonomy Level
Guided Full Ownership
Communication Load
Independent Highly Collaborative
AI Insight The role demands advanced technical expertise in civil engineering, Python, and specialized simulation tools, plus the ability to design and tune complex problems for AI models, making it highly challenging.

Salary Analysis

Median Market Rate
USD72,000
US Market
USD60k – 120k
0 USD132k
AI Insight The offered salary of up to $36 per hour translates to approximately $72,000 annually at full-time equivalence, which is near the lower end of the market for civil engineering roles with Python skills. However, as a freelance project, this rate is competitive given the flexibility and non-permanent nature. The market range for similar hybrid roles is $60,000 to $120,000, so the offered amount aligns with entry to mid-level positions.

Key Skills

Civil Engineering Python OpenSeesPy AI Training Problem Design Finite Element Analysis Freelance Technical Writing Simulation Machine Learning

Dear Hiring Team,

I am excited to apply for the Civil Engineer & Python Expert - Freelance AI Trainer position. With a degree in Civil Engineering and over three years of experience integrating Python into structural analysis, I am well-prepared to design challenging computational problems for AI models. My proficiency with OpenSeesPy and CalculiX allows me to create problems that require specialized solvers, ensuring robust evaluation of frontier AI systems.

In my previous role, I developed automated simulation workflows and tuned parameters to achieve convergence in complex finite element models. This experience aligns with the calibration process described, where patience and precision are key to achieving a 10-30% pass rate. I am eager to gain hands-on intuition for how AI agents navigate engineering challenges while deepening my own tool expertise.

Thank you for considering my application. I look forward to contributing to your team and advancing AI evaluation in civil engineering.

Sincerely,
[Your Name]

Can you describe your experience with Python in a civil engineering context? Provide an example of a simulation you automated.
I have used Python extensively to automate finite element analyses with OpenSeesPy. For instance, I wrote a script that parametrically varied column dimensions and rebar layouts to optimize a bridge pier for seismic loads, reducing manual effort by 80%.
How would you design a problem that requires a specialized solver like CalculiX rather than generic numerical libraries?
A problem involving nonlinear contact mechanics with large deformations would be ideal. Generic libraries lack robust contact algorithms, while CalculiX has specialized solvers for such scenarios. I would define a rolling wheel on a deformable surface and require the model to compute the contact pressure distribution.
What approach would you take to tune the difficulty of a problem to achieve a 10-30% pass rate?
I would start with a moderately complex problem and run batch tests. If the pass rate is too high, I would tighten tolerances or add extra constraints like specific boundary conditions. If too low, I would simplify the geometry or provide more explicit input parameters. Iterative adjustments based on agent performance patterns are key.
How do you ensure your problems are verifiable by code and not ambiguous?
I define a clear numerical answer with a precise tolerance, and I write a reference solution that outputs the expected value. The problem statement includes all necessary parameters and avoids subjective interpretation. For example, 'Compute the maximum deflection at node 27 under a given load' with a tolerance of 0.1%.
Describe a time you had to learn a new tool independently. How did you go about it?
When I needed to use YADE for a granular flow project, I started with the official documentation and tutorials, then replicated examples from research papers. I also joined online forums to troubleshoot issues. Within two weeks, I was able to run custom simulations and validate results against experimental data.

Please submit your CV in English and indicate your level of English proficiency.

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.

What this opportunity involves

You design computational engineering problems to challenge a frontier AI model. The problem must have an answer verifiable by code, and the problem has to require a specialized tool like OpenSeesPy, CalculiX, YADE, bempp-cl, or others. Generic numerical libraries on their own won’t cut it. Each problem runs inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model’s answer.
As an expert author, you:
• Pick an anchor tool and design a problem that hinges on its solvers, simulation kernels, or domain-specific models.
• Write a Python reference solution, supply input files and geometry definitions where needed.
• Decide the numerical answer and how close the model needs to get — with a domain-appropriate tolerance — to count as right.
• Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts.
• Once you’re happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield. They will provide feedback to ensure task quality is high.
Calibration requires patience. You’re tuning the problem against batches of parallel runs of the agent, aiming for a pass rate in the 10–30% band. Reaching that means rewriting load cases, tightening boundary conditions, and watching how the agents act. You’ll learn how these agents cut corners, where a simulation stalls, where a solver converges. This time compounds in two directions. You come out of each task with deeper command of the anchor tool itself, and also get a hands-on working intuition for how a frontier model navigates complex structural and geotechnical problems.

What we look for

This opportunity is a good fit for engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:
• Degree in Civil Engineering or related field;
• 2+ years of research, applied, or teaching experience;
• Python proficiency for writing reference solutions;
• Fluency with — or strong willingness to independently learn — at least one scriptable civil engineering package: OpenSeesPy, CalculiX, YADE, bempp-cl, or similar tools from the broader engineering catalogue;
• Ability to design problems that genuinely require a specialized solver;
• Strong written English (C1+).
No prior experience with the listed tools? You’re still welcome to apply — as long as you’re ready to get up to speed on your own and hit the ground running.

How it works

Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid

Project time expectations

For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.

Compensation

On this project, contributors can earn up to $36 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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