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PhD Physicist - Frontier Reasoning Trajectory Writer (AI Trainer)

GraduateJuniorMid levelSeniorContract$110 - $165/hourFrom 15 hours/weekRemoteInformation technologyEducationPosted 4 days agoVerified 4 days ago
Pay for this job is USD $80-$120 per hour, which is approx AUD $110-$165. It will be subject to exchange rates.
Pending fitX of Y criteria met

About the job

This job is about producing "golden trajectories" for challenging physics problems, where you'll create thorough, step-by-step solution walkthroughs. Your work will significantly impact the training of AI systems, ensuring that every reasoning step is sound and organized. The team values collaboration and iteration, working together to refine solutions and enhance the learning experience.

Meet the team

About the company

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Think you're a good fit?See what the hiring team are looking for

You'll be responsible for

✨

Producing golden trajectories

Creating complete, step-by-step solution walkthroughs for hard physics problems, ensuring every reasoning step is sound and the final answer is correct.
πŸ€–

Iterating with frontier LLM

Collaborating with a frontier LLM to draft and refine reasoning for physics problems, producing clean trajectories for training data.
πŸ“„

Detail-oriented derivation production

Producing publication-grade derivations with a keen eye for detail and organization.

Key criteria

πŸŽ“

PhD in physics

Demonstrated completion of a PhD in physics or nearing completion.

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πŸ“œ

Fluency in LaTeX

Proven experience using LaTeX for complex derivations.

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🐍

Python programming skills

Experience with Python for numerical checks and data plotting.

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A meaningful career starts with a match

View your fit

5 criteria for this job
PhD in physics
Demonstrated completion of a PhD in physics or nearing completion.

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Fluency in LaTeX
Proven experience using LaTeX for complex derivations.

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Python programming skills
Experience with Python for numerical checks and data plotting.

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Iterative LLM workflow comfort
Comfortable refining models and identifying reasoning errors.

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Detail-oriented derivations
Experience producing publication-grade, organized derivations.

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