Project Based Recruitment | Scientific Coding - Materials Science & Python | Remote
The project focuses on developing rigorous scientific coding tasks for frontier AI models, combining materials science knowledge with scientific programming, problem design, Python implementation, and AI evaluation.
Selected contributors will create complex scientific problems, develop verified Python solutions, build comprehensive unit tests, and design test cases that can distinguish accurate AI-generated solutions from incorrect ones.
This is an intensive 8-week contractor project requiring up to 40 hours per week and a minimum of four hours of overlap with PST. Candidates from Indonesia and several other eligible countries can apply.
For professionals exploring remote wfa jobs, this project offers a particularly relevant opportunity to combine academic STEM expertise with AI model training.
Job Information
Position: Scientific Coding – Materials Science and Python
Project Type: Project-Based Contractor / Freelancer
Work Arrangement: Remote
Contract Duration: 8 weeks
Work Commitment: 40 hours per week
Time Zone Requirement: 4 hours of overlap with PST
Primary Skill: Materials and Applied Chemistry
Programming Language: Python
Eligible Locations: Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Turkey, and Vietnam
Registration Link: REGISTRATION LINK HERE
What Is This Scientific Coding Project?
The project is focused on building high-quality scientific datasets that can be used to train and evaluate advanced AI systems.
As a scientific coding contributor, you will transform complex STEM concepts into structured programming challenges that AI models must solve.
The work goes beyond simply writing questions. Each task needs to be scientifically accurate, logically structured, deterministic, testable, and sufficiently challenging to distinguish strong model reasoning from incorrect or incomplete solutions.
You will therefore work at the intersection of materials science, scientific computing, Python programming, and AI evaluation.
Key Responsibilities
The selected contributor will be responsible for creating and validating scientific coding tasks.
Main responsibilities include:
Write scientific problem specifications consisting of one main problem and at least three connected sub-problems.
Design sub-problems that progressively build toward the main solution.
Implement verified golden solutions using Python.
Provide complete unit test coverage.
Create discriminative test cases that distinguish correct and incorrect model outputs.
Validate scientific correctness and computational logic.
Run quality-control checks through the task platform.
Complete structural validation and quality-rubric checks.
Improve tasks based on quality-control feedback.
Optimize tasks for Pass@K evaluation across multiple AI model judges.
Maintain high-quality submissions with minimal rework.
Participate in project reviews, feedback sessions, and standups during required overlap hours.
Materials Science and Python Expertise
This project requires more than general Python knowledge.
Candidates should be able to translate scientific concepts into computational problems and implement reliable solutions.
A strong candidate should understand how to formulate scientific assumptions, define constraints, establish expected outputs, and ensure that computational results are scientifically meaningful.
Experience with scientific Python libraries such as NumPy, SciPy, and SymPy can be particularly valuable.
Domain-specific scientific tools and computational materials science experience may also strengthen an application.
Scientific Problem Development
One of the most important parts of this project is creating well-structured scientific problems.
Each task should contain a main problem supported by multiple sub-problems that are logically connected.
The sub-problems should progressively develop the concepts or calculations required to reach the final solution.
Problems must be:
Scientifically correct
Clearly defined
Deterministic
Reproducible
Computationally testable
Logically connected
Sufficiently challenging
Free from ambiguous requirements
The quality of the problem specification directly affects how useful the resulting dataset is for AI training and evaluation.
Python Golden Solutions and Testing
Contributors will also implement verified reference solutions in Python.
These solutions serve as the expected computational result against which AI-generated solutions can be evaluated.
Complete unit-test coverage is therefore an important part of the workflow.
The contributor must ensure that the implementation behaves correctly across expected inputs and that the testing strategy can identify incorrect outputs rather than simply confirming obvious successful cases.
Strong scientific programming practices and careful validation are essential.
Designing Discriminative Test Cases
Another important responsibility is developing test cases that effectively separate correct and incorrect AI solutions.
A weak test may allow an incorrect solution to pass.
A well-designed test case should expose errors in:
Scientific reasoning
Mathematical implementation
Numerical calculations
Boundary conditions
Assumptions
Algorithmic logic
Expected outputs
Deterministic behavior
This makes experience with scientific computing and AI evaluation particularly useful for the project.
AI Model Evaluation
The completed scientific coding tasks will be evaluated against multiple large language models.
The workflow includes quality checks designed to determine whether the tasks meet the required standards for AI evaluation.
Contributors will iterate on their work based on quality-control feedback and are expected to maintain a strong first-submission approval rate.
This means the role combines scientific authorship with AI benchmark development rather than functioning as a conventional research or software engineering position.
Required Qualifications
Candidates should have a strong academic or professional background in Materials Science or a closely related discipline.
The core requirements include:
Master's or PhD degree in Materials Science or a related field.
Strong Python programming skills.
Experience with scientific computing.
Ability to formulate rigorous scientific problems.
Ability to define clear constraints and expected outputs.
Strong attention to detail.
Understanding of scientific correctness and determinism.
Experience with AI data annotation, research, or scientific writing.
Familiarity with LLM evaluation frameworks or coding benchmarks.
Experience with scientific Python libraries such as NumPy, SciPy, or SymPy.
Published research or academic project experience in a STEM field.
Who Is This Project For?
This opportunity may be particularly suitable for:
Materials Scientists
Computational Materials Scientists
Applied Chemists
Scientific Programmers
STEM Researchers
Research Scientists
Scientific Computing Specialists
Python Developers with strong STEM backgrounds
AI Data Trainers with materials science expertise
Researchers experienced in computational modeling
Candidates who enjoy translating complex scientific concepts into precise computational problems may find this project especially relevant.
Why This Project Is Interesting
The demand for high-quality scientific data is growing as AI models become increasingly capable of solving technical and research-oriented problems.
However, creating useful scientific AI benchmarks requires subject-matter experts who understand both the underlying science and the computational requirements needed to evaluate AI outputs reliably.
This project provides an opportunity to contribute to that process by creating materials science coding challenges designed specifically for advanced AI training and evaluation.
For STEM professionals interested in the intersection between research and artificial intelligence, the project can provide practical experience in a rapidly developing area of AI infrastructure.
Contract and Working Requirements
The engagement is structured as a contractor or freelancer assignment.
The expected commitment is:
40 hours per week
4 hours of overlap with PST
8-week contract
Remote working arrangement
No medical or paid leave under the contractor arrangement
Because the project requires a substantial weekly commitment, applicants should confirm their availability before registering.
Application and Selection
Interested candidates should be prepared to demonstrate their academic, research, scientific programming, and analytical capabilities.
The project places significant emphasis on scientific accuracy, task quality, testing, and the ability to follow detailed evaluation standards.
Candidates with previous experience in AI data annotation, scientific writing, coding benchmarks, or LLM evaluation may have an advantage.
Registration
Interested candidates can submit their application through the registration link below.
Registration Link: REGISTRATION LINK HERE
If you meet the Materials Science and Python requirements and can commit to the required schedule, consider registering promptly.
The project has a defined eight-week engagement and requires contributors who can begin working with a substantial weekly commitment.
Why Apply Early?
Project-based AI opportunities requiring advanced STEM expertise can attract a limited pool of qualified candidates.
This role combines a specialized academic background with Python, scientific computing, and AI evaluation skills, making the candidate profile relatively specific.
If your background matches the requirements and you are located in one of the eligible countries, it is worth preparing your application early rather than waiting until the project moves further into its recruitment process.
Registration Link: REGISTRATION LINK HERE
You can also explore additional opportunities through remote wfa jobs for professionals seeking remote and flexible project-based work.
Final Thoughts
The Project-Based Scientific Coding – Materials Science and Python opportunity is designed for STEM professionals who want to contribute their scientific and programming expertise to advanced AI training.
The work combines materials science, Python, scientific problem solving, unit testing, benchmark development, and LLM evaluation in a focused eight-week engagement.
If you have a Master's or PhD in Materials Science or a related discipline, strong Python skills, scientific computing experience, and an interest in AI evaluation, this project is worth considering.
With a 40-hour-per-week commitment and a required PST overlap, applicants should ensure they can meet the schedule before submitting their registration.
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