Life Sciences Research Assistant

$30–50/hrrate as listed

You'll write expert-level evaluation tasks in the life sciences — experimental design critiques, protocol analyses, and primary data interpretation — using authentic research artifacts rather than textbook scenarios. Each task ships with a multi-item rubric that an LLM grader can apply to judge methodological rigor, data interpretation, and source fidelity.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

micro1 is staffing Life Sciences Research Assistants for a customer building next-generation AI benchmarks. A typical assignment starts with you sourcing real material — a raw dataset, a published protocol, a supplementary file, a figure with its underlying numbers — and building a problem around it that a strong AI agent should be able to solve but usually can't. That means multi-step reasoning: read the protocol, spot the missing control, interpret the data given the flaw, and state what the result actually supports. You then document the correct methodological approach and biological interpretation in full, and write a rubric with enough discrete, checkable items that two independent graders would score a response the same way.

The emphasis is on authenticity. Tasks that reduce to a definition, a single calculation, or a hypothetical with a clean answer generally get rejected. What the project wants is the ambiguity of real bench and analysis work — underpowered experiments, batch effects, protocols that omit a step, conclusions that outrun the data.

What the platform screens for

Verifiable hands-on research. Expect follow-ups on specific assays, instruments, and analysis pipelines you've actually run, and on what went wrong when you ran them.
Methodological judgment. Can you articulate why a design is weak, not just that it is, and what the minimum fix would be?
Rubric discipline. Screens probe whether you can decompose a correct answer into objective criteria instead of writing "demonstrates good understanding."
Writing under constraint. Clear scientific English, precise about uncertainty, no filler.

No AI or ML background is required or expected. Domain depth is the qualification.

Logistics

Fully remote, contractor engagement, asynchronous — you work when you want and exchange feedback with project coordinators in writing. Compensation is output-based: paid per accepted task meeting spec, with observed rates in the $30–50/hr range depending on how fast you work and how often submissions clear review on the first pass. Rates are as observed and not guaranteed. A weekly minimum submission count applies. Hiring moves fast — roles are often filled within 48 hours, and selected experts are expected to start tasks within 24–48 hours of onboarding.

What they look for

Methodological rigor
Source synthesis
Rubric fidelity
Data interpretation
Protocol fidelity

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you hold a Bachelor's degree or higher in biology, biomedical sciences, biochemistry, or a closely related discipline?
Life Sciences Degree · required
Do you have a minimum of 2 years of hands-on research or laboratory experience involving primary data analysis and scientific protocols?
Hands-on Research Experience · required
Have you drafted or reviewed scientific documentation (such as lab reports, protocols, or research manuscripts)?
Scientific Documentation · preferred
Have you worked with diverse data formats including CSVs, spreadsheets, PDFs, and technical documents in a research or laboratory context?
Data Format Handling · preferred

0 of 4 answered

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether the candidate can articulate the difference between textbook protocol design and real-world feasibility; whether they've actually done this hands-on, not just read about it.
A strong answer: The answer must name a specific protocol or experimental design they personally worked on, identify at least two potential failure points they caught or designed around (e.g., buffer pH, sample storage, statistical power), and explain how they validated that the approach was sound before or during execution.
Where people lose it: Candidates who describe a protocol they followed rather than one they designed or critiqued; or who list generic good practices (e.g., 'use controls') without connecting to a concrete, lived example.