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Principal Research Scientist (Computer Science / Computational Physics)

micro1 Micro1 Part-timeRemote
Posted Oct 13, 2026 · ✓ Still open — checked 1 hour ago

Quick facts

Type
Part-time
Location
Remote
Skills
Physics
Platform
Micro1 — 245 open jobs
Typical pay at Micro1
$25–150/hour · median $90 (17 jobs on our site)
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Required Skills
Novelty judgment
Research direction
Computational rigor
Complex problem solving - Research
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: Principal Research Scientist (Computer Science / Computational Physics)

Role Type: Contractor (flexible, Part-Time or Full-Time)

Location: Remote

micro1 is engaging Principal Research Scientist (Computer Science / Computational Physics)s to contribute to an advanced customer project at the intersection of computational theory and AI. 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. You will work with frontier AI models and agents to tackle open problems, drive research direction, and define standards for scientific breakthroughs. Your insights and rigorous approach will be pivotal in charting what counts as genuine progress within your subfield.

Scope of Work

Identify, frame, and articulate foundational open problems, theoretical bounds, or simulation challenges where verifiable progress can be demonstrated.
Guide AI models and agents in multi-step research processes—ranging from proofs and algorithms to numerical experiments and high-performance simulations.
Critically assess and validate results, utilizing proof checks, computational reproduction, and formal methods to pinpoint the limits of model-generated reasoning.
Evaluate and document the novelty, correctness, and significance of research outputs relative to current literature and established results.
Produce clear, concise research memos detailing research attempts, partial solutions, and observed model limitations or failure patterns.
Advise on research directions, recommending promising subfields and open problems suitable for advancement via AI models and computational tools.

Preferred Qualifications

7 - 10+ years of active research in computer science or mathematical/computational physics, with a strong record of scholarly contributions.
Recognition at the field level—such as major prizes, society fellowships, or plenary/invited talks at top scholarly events.
A consistent publication record in leading venues within your domain, with recent results published in the past year.
Demonstrated success resolving named open problems or introducing influential concepts adopted by peers.
Deep, hands-on experience with computational research methods: simulation, numerical analysis, algorithmic implementation, and/or high-performance computing environments.
Proven ability to rigorously examine technical work outside your immediate area of expertise.
Experience on program committees, editorial boards, or with formal research tools (e.g., Lean, Coq, SAT/SMT solvers) is a strong plus, as is a public record of outlining open problems or showcasing research with advanced AI systems.
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About Micro1

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.