Micro1
Micro1Verified listing
RemoteAI & Machine LearningResearch Engineering

Computational Physics Expert | $40-$60/hr Remote

40–60/hr
Remote
Posted August 3, 2026
contract
10 openings
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Listing checked September 1, 2026 · pay as published by Micro1

Overview

This role sits at the intersection of computational physics and artificial intelligence. You’ll apply your deep scientific expertise to help train next-generation AI systems, ensuring they reason accurately and learn from high-quality real-world physics data. Your work will directly influence how models handle complex simulations, numerical modeling, and scientific analysis.

What You'll Do7

  • 1Use your advanced knowledge of computational physics to produce high-quality insights that improve AI evaluation projects.
  • 2Analyze, interpret, and synthesize large-scale scientific datasets and experimental outcomes to inform model training.
  • 3Develop, review, and refine quantitative models, simulations, or workflows across relevant physics domains such as particle physics or astrophysics.
  • 4Collaborate with interdisciplinary teams to enhance AI model capabilities in scientific reasoning and problem-solving.
  • 5Apply advanced numerical methods and computational techniques—like Monte Carlo methods or HPC workflows—to tackle real-world physics challenges.
  • 6Document findings and clearly communicate complex scientific concepts through both written reports and verbal discussions.
  • 7Uphold rigorous standards of scientific integrity and accuracy in all deliverables.

Requirements7

  • 1A PhD in physics or a closely related field, or equivalent industry/research experience.
  • 2Proven expertise in computational physics, scientific computing, or simulation-heavy research.
  • 3Strong programming skills in Python, C++, and MATLAB, plus familiarity with scientific tools like ROOT.
  • 4Hands-on experience with numerical simulations, Monte Carlo methods, or HPC workflows.
  • 5Exceptional analytical, quantitative, and mathematical reasoning abilities.
  • 6Demonstrated ability to perform experimental or scientific data analysis.
  • 7Excellent written and verbal communication skills, with the capacity to explain complex topics to diverse audiences.

Who Should Apply

This role is ideal for a PhD-level physicist or computational scientist who thrives at the frontier of physics and AI. You likely have a background in particle physics, astrophysics, plasma/fusion, or quantum systems, and you’re comfortable translating your research into practical contributions that improve machine learning models. A track record of published research and experience with AI/ML workflows or scientific benchmarking will set you apart.

Salary Insight

The role offers $40–$60 per hour as a contractor position, with fully remote work.

Pay and demand for Machine Learning & AI roles

Aggregated

Typical pay

$70/hour

This role

$40–$60/hr

Most Machine Learning & AI roles pay $50–$100 per hour. This role's pay falls inside that range.

Based on 638 similar roles that publish pay · 104 publish 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.

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Most requested skills · share of roles

  • python
    14%
  • llm evaluation
    7%
  • ai training
    6%
  • quality assurance
    6%

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Location

TypeRemote
LocationRemote
This is a remote position

Compensation

$40–60/hr

Required Skills

computational physicsscientific computingpythonc++matlabrootnumerical simulationsmonte carlo methodshpc workflowsdata analysisquantitative modelingscientific data interpretationanalytical reasoningmathematical reasoningscientific communicationwritten communicationverbal communicationteam collaborationscientific integrityai/ml workflowsscientific benchmarking

Application Tip

When applying, include a brief example of a project where you used numerical simulations or Monte Carlo methods to solve a physics problem and explain how that work could inform AI training. Highlight any experience you have with scientific benchmarking or evaluating model outputs.

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