Mercor
MercorVerified listing
Remote

Physics PhD Coding Experts | $70/hr Remote

70/hr
Remote
Posted August 24, 2026
hourly
81 openings
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Listing checked September 9, 2026 · pay as published by Mercor

Overview

This six-week contract places you inside a benchmark build for leading AI labs. You will author original, executable physics problems that current frontier models cannot crack, sourcing material from published papers, open-source repositories, or your own designs. The workflow runs through a Git-based pull-request system with automated checks, so comfort with Git and Docker is essential. You'll earn $70 per hour while contributing around 20 hours each week, fully remote.

What You'll Do4

  • 1Select or construct research-grade source material from published papers, Kaggle datasets, open-source code, or your own experimental designs.
  • 2Craft precise scientific prompts that test the reasoning limits of state-of-the-art AI systems in your chosen physics specialization.
  • 3Design the grading criteria and acceptance tests that define each correct answer without ambiguity.
  • 4Run your tasks against leading frontier models and adjust difficulty until those models fail more often than they succeed.

Requirements5

  • 1Hold a PhD in physics, applied physics, or a related discipline.
  • 2Possess deep working knowledge in at least two of: condensed matter, optics, quantum information, quantum computing, computational physics, astrophysics, or particle physics.
  • 3Write production-grade scientific code in Python, R, or another mainstream language for computational research.
  • 4Handle Git/GitHub for version control and run code inside Docker containers.
  • 5Preferred: have peer-reviewed publications or prior scientific software engineering experience.

Who Should Apply

This role suits a research scientist who enjoys constructing difficult, well-specified problems more than solving them. You already work with code in your own research and are comfortable sharing via Git. It is less appropriate for those who prefer open-ended investigation without the discipline of writing reproducible tasks. A common reason candidates miss out is a lack of direct Docker experience or an inability to describe how they would craft prompts that challenge current AI models. Also, if your expertise covers one subdomain only, you will need to demonstrate how you relate it to a second, since the role expects depth in at least two areas.

Salary Insight

The engagement pays $70 per hour, and you will bill for each hour worked. With the 20-hour minimum, that comes to at least $8,400 over the six weeks, paid out on a weekly cadence. The fixed rate reflects the specialized nature of the work, which expects both research-level physics and production coding skills.

Pay and demand for Physical Sciences roles

Aggregated

Typical pay

$84/hour

This role

$70/hr

Most Physical Sciences roles pay $70–$119 per hour. This role's pay falls inside that range.

Based on 26 similar roles that publish pay · 1 publishes only a top rate; those count at the rate they gave

Typical rangeMedian payThis role

Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.

Live similar roles
28
Listed in last 30 days
5
Remote
96%

Hiring most right now: micro1 (17) · Mercor (8) · Turing (2)

Most requested skills · share of roles

  • Physics
    39%
  • chemistry
    11%
  • technical writing
    11%
  • thermodynamics
    11%

Figures from Physical Sciences roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.

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Location

Typeremote
LocationRemote
This is a remote position

Compensation

$70/hr

Required Skills

physicscondensed matteropticsquantum informationquantum computingcomputational physicsastrophysicsparticle physicspythonrgitgithubdockerscientific computingai evaluationprompt designgrading criteriamodel calibration

Application Tip

When you apply, include a one-page example of a research problem you would convert into an evaluation task, showing step by step how you would calibrate it against a frontier model. Highlight your use of Git and Docker in that example, since those tools are immediate screening filters.

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