Quick facts
- Pay
- $100 - $170/hour
- Type
- Part-time
- Location
- Remote
- Background
- PhD
- Skills
- Physics
- Platform
- Micro1 — 349 open jobs
- Typical pay at Micro1
- $20–180/hour · median $68 (340 jobs on our site)
- Payout
- Deel (twice a month)
- Apply
- Free to apply — never pay to get AI training work
Pay: $100 - $170/hour
Required Skills
Physics
Theoretical Physics
Mathematical Physics
Quantum Physics
LaTeX
About micro1
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.
Role Title: Physics Expert (PhD / Postdoc)
Role Type: Contractor
Location: Remote
micro1 is engaging Physics Experts (PhD / Postdoc) to contribute their advanced knowledge to a frontier research-level physics benchmark, supporting a customer’s innovative project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. This engagement spans a range of physics subfields, including mathematical physics, many-body and condensed matter theory, statistical physics, quantum information, and theoretical physics. Contributors may be considered for distinct roles such as Solver, Auditor, or Adjudicator, with assignments based on expertise, seniority, and demonstrable hands-on experience.
Scope of Work
Produce and validate high-quality solutions to advanced theoretical and mathematical physics problems across multiple subfields.
Audit, review, and provide constructive feedback on solutions contributed by peers within the physics benchmark.
Adjudicate complex cases by evaluating conflicting solutions and determining accuracy based on rigorous standards.
Document methodologies, reasoning, and solutions clearly using LaTeX and other scholarly formats.
Collaborate asynchronously with project leads and fellow experts to uphold project quality and meet key deliverables.
Ensure all work reflects up-to-date knowledge of contemporary research and methods in core physics domains.
Assist in refining benchmark protocols and question sets to enhance the accuracy and relevance of physics evaluations.
Preferred Qualifications
PhD or postdoctoral research background in physics, theoretical physics, quantum physics, or mathematical physics.
Proven expertise in one or more relevant subfields: mathematical physics, many-body theory, condensed matter, statistical physics, or quantum information.
Exceptional analytical and problem-solving skills with hands-on experience in solving and articulating advanced physics problems.
Proficiency in written and verbal communication, including experience preparing technical documentation and scholarly content (LaTeX essential).
Prior participation in research collaborations, academic peer review, or expert panels (a strong plus).
Demonstrated ability to work independently and asynchronously within a remote, distributed project setting.
Meticulous attention to detail and a commitment to maintaining rigorous scientific standards in all contributions.
Email
By clicking any of the "Continue" buttons below, you acknowledge and agree to our Terms and Privacy Policy for candidates.
Continue
or
Continue with Google
Continue with LinkedIn
Have any questions? See FAQs
micro1 recruits engineers and domain experts for AI labs using its AI interviewer. Once vetted, contractors work on human-data projects such as writing and checking model outputs or building coding evaluations.