Physics Expert - AI Training Project (India)
Listing checked September 18, 2026 · pay as published by Handshake AI
Overview
Handshake AI hires computational and theoretical physicists for part-time, project-based work that improves how AI models handle advanced physics. You will design expert-level problems and scenarios drawn from numerical simulation, HPC, and your own subfield. Then you review AI-generated answers for technical accuracy, numerical rigor, and alignment with real modeling practice. The schedule stays open: no set hours and no minimum commitment, so the work fits around a current research or industry role.
What You'll Do6
- 1Build expert-level physics problems and scenarios that mirror real simulation, modeling, and computational workflows.
- 2Review AI-generated responses for technical accuracy, numerical rigor, and fidelity to how physicists work in practice.
- 3Write structured feedback that helps AI models improve their reasoning in your domain.
- 4Apply subfield depth in areas such as condensed matter, astrophysics, high-energy physics, or fluid dynamics.
- 5Use PDE/ODE solvers, Monte Carlo methods, or HPC practices when you assess model output.
- 6Work at your own pace with no fixed schedule or minimum hour target.
Requirements11
- 1Indian work authorization.
- 2PhD in Physics with a computational or theoretical focus.
- 3First-author publications in a computational subfield or 2+ years of hands-on industry experience.
- 4Demonstrated depth in numerical simulation, PDE/ODE solvers, or Monte Carlo methods.
- 5HPC and scientific computing experience with tools such as NumPy, SciPy, C++, or Fortran.
- 6Expertise in a core subfield like condensed matter, astrophysics, high-energy physics, or fluid dynamics.
- 7Clear written explanations of complex physics concepts.
- 8Independent work in a remote, asynchronous setting.
- 9Strong attention to detail.
- 10Bonus: running large-scale simulations on HPC clusters across multiple scientific computing environments.
- 11Bonus: lattice methods or other specialized techniques in your subfield.
Who Should Apply
Physicists who hold a PhD with a computational or theoretical focus and can point to first-author papers or at least two years of industry work will fit this project. You should also bring real depth in one area such as numerical simulation, Monte Carlo methods, HPC, condensed matter, astrophysics, high-energy physics, or fluid dynamics. The role suits someone who writes clear explanations of complex physics and can work alone on an asynchronous schedule. Candidates without Indian work authorization will not move forward, and applicants who treat this as a full-time job or lack written communication examples often score low. A common rejection reason is a profile that lists physics knowledge but shows no concrete simulation, solver, or HPC work.
Salary Insight
The listing sets pay at $47.00 per hour. That rate applies to part-time, project-based tasks, so total earnings depend on how many assignments you complete. For PhD-level physics work in AI training, $47.00 per hour sits in a strong range, though the source gives no details on task volume or project length.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$75/hour
This role
$47/hr
Most Machine Learning & AI roles pay $55–$100 per hour. This role's pay sits below that range.
Based on 526 similar roles that publish pay · 91 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
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- Listed in last 30 days
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Most requested skills · share of roles
- python17%
- technical writing9%
- llm evaluation8%
- data annotation7%
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Compensation
$47/hr
Required Skills
Application Tip
Lead with your strongest qualification match: name your first-author publications or 2+ years of industry work, then list the exact tools and methods you use, such as NumPy, SciPy, C++, Fortran, Monte Carlo methods, or HPC clusters. Add a short writing sample that explains one complex physics result in plain language, because structured feedback and clear explanations are central to the role.
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