TuringDomain experts
Materials & Semiconductor Physics Technical Lead
Pay not published
About this role, in Turing's words
We are seeking an experienced Materials Science or Semiconductor Physics Expert to evaluate and benchmark advanced AI models against real-world materials discovery workflows. Working directly with client R&D teams, you will turn atomic-scale engineering challenges—spanning thin-film deposition (ALD, CVD, PVD), etch, planarization, and multiscale physics simulations (DFT, MD, kMC)—into structured test problems, define scientific grading criteria, and diagnose why frontier AI models succeed or fail.
What you'll do
Key Responsibilities
- Support technical discovery sessions with client’s R&D teams to map and deconstruct the end-to-end materials discovery lifecycle into discrete sub-processes.
- Convert real materials engineering challenges into structured test scenarios with defined inputs, constraints, and verified golden reference solutions.
- Define clear scoring rubrics and programmatic validation rules (e.g., verifying stoichiometry, thermodynamics, and non-diverging simulation parameters).
Show all 5 itemsHide the other 2 in Key Responsibilities
- Inspect step-by-step AI reasoning traces to diagnose failure root causes.
- Scope out domain-specific data generation requirements, synthetic physics pipelines, and fine-tuning strategies to advance AI model performance.
Candidate background
Qualifications
- PhD or Master's degree in Materials Science, Applied Physics, Chemical Engineering, Microelectronics, or a related discipline.
- Deep technical expertise in atomic-scale materials engineering: precision thin-film deposition (ALD, CVD, PVD), plasma etch, CMP, 3D packaging, or advanced semiconductor memory/logic architectures.
- Hands-on familiarity with computational physics/chemistry modeling pipelines: Density Functional Theory (DFT), Molecular Dynamics (MD), or Kinetic Monte Carlo (kMC) simulation setups.
- Proven ability to formulate complex, open-ended scientific workflows into structured, verifiable problem statements with clear ground-truth criteria.
- Strong verbal & written communication skills required during technical and business workshops.
Quoted from selected sections of Turing'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 Turing's words
Posted by Turing, reproduced here so you can judge the role before clicking. Original posting ↗
About Turing & Role
Turing is one of the world's leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent. We are staffing a frontier AI data initiative building the infrastructure and training data used to develop and evaluate AI agents. You will be assigned to one of two tracks: connectors or tasks.
Role Overview
We are seeking an experienced Materials Science or Semiconductor Physics Expert to evaluate and benchmark advanced AI models against real-world materials discovery workflows. Working directly with client R&D teams, you will turn atomic-scale engineering challenges—spanning thin-film deposition (ALD, CVD, PVD), etch, planarization, and multiscale physics simulations (DFT, MD, kMC)—into structured test problems, define scientific grading criteria, and diagnose why frontier AI models succeed or fail.
Key Responsibilities:
• Support technical discovery sessions with client’s R&D teams to map and deconstruct the end-to-end materials discovery lifecycle into discrete sub-processes.
• Convert real materials engineering challenges into structured test scenarios with defined inputs, constraints, and verified golden reference solutions.
• Define clear scoring rubrics and programmatic validation rules (e.g., verifying stoichiometry, thermodynamics, and non-diverging simulation parameters).
• Inspect step-by-step AI reasoning traces to diagnose failure root causes.
• Scope out domain-specific data generation requirements, synthetic physics pipelines, and fine-tuning strategies to advance AI model performance.
Qualifications:
• PhD or Master's degree in Materials Science, Applied Physics, Chemical Engineering, Microelectronics, or a related discipline.
• Deep technical expertise in atomic-scale materials engineering: precision thin-film deposition (ALD, CVD, PVD), plasma etch, CMP, 3D packaging, or advanced semiconductor memory/logic architectures.
• Hands-on familiarity with computational physics/chemistry modeling pipelines: Density Functional Theory (DFT), Molecular Dynamics (MD), or Kinetic Monte Carlo (kMC) simulation setups.
• Proven ability to formulate complex, open-ended scientific workflows into structured, verifiable problem statements with clear ground-truth criteria.
• Strong verbal & written communication skills required during technical and business workshops.
Benefits
• Opportunity to work on cutting-edge AI projects.
• Competitive compensation.
• Flexible working hours and remote work environment.
Application Process
• Shortlisted candidates will need to go through online interviews.
• Once the interview is cleared, candidates can start.
Working with TuringPay, getting started and work, from Turing's own pages
What the platform publishes in general. These are not extra terms confirmed for this particular role.
- Getting paid
- Paid monthly, around the 5th, straight to your bank account, or inside Deel on projects run through Deel. Its help articles disagree on whether that is the 5th calendar day or business day. help.turing.com/articles/9691407757-project-onboarding, help.turing.com/articles/6582454854-what-if-i-haven-t-rec..., help.turing.com/articles/1226770947-project-offboarding
- Before your first paid task
- A short application, with tests on some roles. Turing's review article says an interview is typically required for all roles. Onboarding covers the terms, ID verification with a driver's license or passport, and tax and payment details. help.turing.com/articles/2020987592-apply-for-jobs-at-turing, help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/9691407757-project-onboarding
- How much work
- Review takes from 48 hours to 6 weeks after you finish applying, and the client makes the final choice. No reply within 6 weeks usually means you were not selected. help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/2020987592-apply-for-jobs-at-turing
- Contract
- Depending on the project, the engagement rests on Turing's Terms of Service and a Service Agreement, on Deel, or on a partner that provides the contract. help.turing.com/articles/6582454854-what-if-i-haven-t-rec...
Read on the pages linked above on 23 Sep 2026. Platforms change these terms, so check the linked page before you rely on one.
How does the pay compare?Across Domain experts roles, not this role alone
From our own daily reads of every open listing on the platforms we follow, not from Turing's posting. How we count.
Pay across the field: Domain experts
Across 693 roles in this field with a stated hourly rate in US dollars, on 18 platforms, the middle half pays $65 to $130 an hour at the top of the range, and the median is $100.
| Platform | Roles | Top of range, per hour |
|---|---|---|
| Alignerr | 149 | $24 to $200 · median $80 |
| micro1 | 130 | $28 to $400 · median $110 |
| AfterQuery | 88 | $45 to $175 · median $100 |
| Handshake AI | 79 | $30 to $300 · median $85 |
| Mercor | 78 | $55 to $450 · median $120 |
Sources and listing historyWhere this information comes from
This listing's history
- First seen here on 24 Sep 2026, and still on Turing's list when we last read it on 13 Oct 2026.
- Posted by Turing
- First seen by AIPayList
- Last seen live
- Listing ID
- 108340
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