micro1Other AI work
Materials Science Expert
up to $150/hr
About this role, in micro1's words
The work involves creating, solving, reviewing, and validating engineering tasks related to material structures, properties, processing, performance, and failure. A representative task may require constructing a material or atomic model, configuring and running a simulation, calculating relevant properties, analyzing the resulting outputs, and determining whether the solution is computationally valid and physically meaningful.
What you'll do
What You’ll Work On
- Solve and validate computational materials-science and materials-engineering problems.
- Create material structures, atomic configurations, compositions, and solver-ready inputs.
- Model relationships between composition, structure, processing, properties, and performance.
Show all 5 itemsHide the other 2 in What You’ll Work On
- Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.
- Use Python to generate inputs, automate calculations, conduct parameter sweeps, process results, and validate outputs.
Candidate background
What this role asks for
- Python
- Materials Science
Read from the posting's own words: show the exact sentences
- Python: “listed by the platform under required skills: Python”
- Materials Science: “listed by the platform under required skills: Materials Science”
Quoted from selected sections of micro1's posting. The full posting may state other conditions. Read the full posting
A closer look
More context, when you need it.
Full job descriptionThe original posting, in micro1's words
Posted by micro1, reproduced here so you can judge the role before clicking. Original posting ↗
Materials Science Expert
Pay: $80–$150/hour
Location: Global, fully remote
Job Type: Contractor (~15 hours per week)
Schedule: Flexible—you choose the hours and days you work, including weekends if desired
We are looking for a highly skilled Materials Science Expert to contribute to an AI training project involving computational materials science, materials modeling, scientific simulation, and Python.
The work involves creating, solving, reviewing, and validating engineering tasks related to material structures, properties, processing, performance, and failure. A representative task may require constructing a material or atomic model, configuring and running a simulation, calculating relevant properties, analyzing the resulting outputs, and determining whether the solution is computationally valid and physically meaningful.
This is a very coding heavy role. Candidates but have experience using coding agents with python in their workflow.
What You’ll Work On
• Solve and validate computational materials-science and materials-engineering problems.
• Create material structures, atomic configurations, compositions, and solver-ready inputs.
• Model relationships between composition, structure, processing, properties, and performance.
• Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.
• Use Python to generate inputs, automate calculations, conduct parameter sweeps, process results, and validate outputs.
Required Qualifications
• An MS or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline; or
• An MS or PhD in Mechanical Engineering or Chemical Engineering with a substantial materials specialization.
• Strong understanding of materials behavior and relevant structure-property relationships.
• Experience with computational materials modeling, simulation, characterization, or materials-focused engineering analysis.
• Experience using coding agents and Python.
Relevant tools may include LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM , or similar programmatic materials and simulation software. Experience with an equivalent CLI-accessible tool is acceptable.
Relevant Python tools may include NumPy, SciPy, pandas, Matplotlib, Jupyter , atomistic modeling packages, materials informatics libraries, or domain-specific scientific tools. No single library is mandatory.
Experience may come from academic research, national laboratories, industry R&D, computational engineering, or other demonstrated materials work.
Process
• Apply to the role and complete the screening questions.
• Complete an AI interview of approximately 30 minutes.
• Complete the hiring manager review.
Compensation Structure
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.
Minimum submission requirements apply.
Start Timeline & Availability
• We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.
Working with micro1Pay, getting started and work, from micro1's own pages
What the platform publishes in general. These are not extra terms confirmed for this particular role.
- Getting paid
- Paid in US dollars twice a month through Deel, usually landing around the 5th and the 20th. Nothing is paid until Deel's identity check is done. library.micro1.ai/working/faqs, library.micro1.ai/working/payroll-policy
- Before your first paid task
- An AI interview with micro1's recruiter Zara, then your profile, ID verification, a KarmaCheck background check and the Deel contract. Some pipelines add a quiz or test task. micro1.ai/experts, micro1.ai/ai-interview-guide, library.micro1.ai/getting-started/next-steps-after-certif..., library.micro1.ai/working/faqs, library.micro1.ai/working/what-to-expect
- How much work
- Certification makes you eligible but does not guarantee placement. micro1's FAQ puts the usual commitment at about 15 to 20 hours a week, depending on the client. micro1.ai/experts, library.micro1.ai/working/faqs
- Contract
- Independent contractor, with the contract issued through Deel. library.micro1.ai/policies/understanding-your-expert-agre..., library.micro1.ai/working/deel
Read on the pages linked above on 23 Sep 2026. Platforms change these terms, so check the linked page before you rely on one.
Alex's experience on micro1A personal account, not a promise about your project
Alexruns this site and has worked on micro1 himself. Written 31 Aug 2026.
- Every interview I took used an AI interviewer, and some included a timed business exercise of around 25 minutes.
- On later applications, interview reuse on micro1 was often partial, and I still answered questions about one to three additional skills.
- I entered a requested rate for the job. One listing showed about $180 an hour. I requested about $160 and received the rate I entered. Those amounts are approximate, and this describes my application, not proof that a lower request caused acceptance.
- Onboarding and setup came to about 2 hours, with clear materials. Of the three platforms I worked on, it was the easiest transition into work. That is an estimate from my projects, not a platform average.
- On my micro1 project, Hubstaff showed time and mouse or keyboard activity, not screenshots, app names or website history. That describes what I saw, not every possible setup.
- I was paid for hours logged, once every 14 days, and I have not experienced late or incorrect payments. That was my arrangement.
One person's applications, not a promise about yours.
What happens after you apply, first-handSources and listing historyWhere this information comes from
This listing's history
- First seen here on 7 Sep 2026, and still on micro1's list when we last read it on 13 Oct 2026.
- Listed pay changed on 16 Sep 2026: up to $130/hr before, up to $150/hr after.
A change can also be a correction to how we read micro1's feed. The figure at the top of this page is always the latest read.
- Posted by micro1
- First seen by AIPayList
- Last seen live
- Listing ID
- ee7c4e95-fa17-410c-a0a9-5d05155a5405
Pay as the source states it: “micro1 job record, ideal_hourly_rate: $80–$150/hr”.
Platforms sometimes change a rate after posting. Every listing here is re-read at least once a day and corrected when its source moves, so a difference you see on the platform is usually a change made since the last read. How freshness works.
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