Bioinformatics Scientist

$90–120/hrrate as listed

micro1 is contracting bioinformatics and computational biology specialists to evaluate and improve AI model outputs on medicinal chemistry, omics, and drug discovery tasks. Work is remote and asynchronous, with observed rates in the $90–120/hr range depending on depth of domain background.

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

You'll spend most of your time reading AI-generated scientific content and judging whether it holds up. That means checking whether a model's reasoning about a structure-activity relationship is chemically sound, whether a proposed analysis pipeline for an RNA-seq dataset makes sense, whether cited mechanisms and targets are real, and whether an interpretation of omics output is defensible or plausible-sounding nonsense. Alongside evaluation, you may be asked to author reference problems, curate and annotate datasets from chemical and biological sources, and write critique that explains why an answer fails — not just that it does.

Tasks arrive in batches through the platform. Typical deliverables include scored comparisons between two model responses, structured error annotations, and short written rationales. The quality bar sits on specificity: reviewers who write "incorrect ADME reasoning" are less useful than reviewers who name the metabolic liability the model missed.

What the platform screens for

Verifiable credentials. A PhD or MSc in bioinformatics, computational biology, medicinal chemistry, or an adjacent field, plus a record you can point to — publications, preprints, thesis, or project work.
Depth under follow-up. Screens probe a claimed specialty with successive questions. Broad familiarity with drug discovery vocabulary rarely survives the third question.
Hands-on tooling. Concrete experience with Python, R, RDKit, KNIME, or standard omics workflows, described at the level of what you actually ran and what broke.
Written clarity. Feedback is the product. Ambiguous prose is a disqualifier regardless of pedigree.

No prior AI or ML experience is expected or evaluated.

Logistics

Fully remote, contractor engagement, asynchronous. Most experts treat this as part-time alongside existing academic or industry work — commonly 10–20 hours per week, though volume fluctuates with project phase and can pause between batches. Pay bands reported by contributors fall in the $90–120/hr range; rates are set per project and are not guaranteed. Expect an onboarding calibration round before paid work begins.

What they look for

medicinal chemistry

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 Master's degree or PhD in Bioinformatics, Computational Biology, Medicinal Chemistry, or a closely related discipline?
Advanced degree in relevant field · required
Have you personally analyzed large-scale omics datasets (e.g., transcriptomics, proteomics, genomics) or cheminformatics datasets (e.g., chemical library screening, molecular property prediction) in a professional or research capacity?
Large-scale omics or cheminformatics dataset experience · required
Are you proficient in at least one of the following programming languages or tools: Python, R, RDKit, or KNIME?
Bioinformatics programming proficiency · required
Have you worked on projects involving medicinal chemistry concepts such as structure-activity relationships (SAR), drug design, or compound optimization?
Medicinal chemistry knowledge · preferred
Are you able to work fully remotely and have you previously completed sustained work on remote or asynchronous projects?
Remote work capability · preferred

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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 has hands-on experience with the scale and type of data this role will require, and whether they can articulate the scientific reasoning behind analytical choices.
A strong answer: The answer must name a specific dataset (transcriptomics, proteomics, chemical library screening, etc.), describe the biological or chemical hypothesis that motivated the analysis, and explain what patterns or insights emerged and why they mattered—not just 'I cleaned the data.'
Where people lose it: Candidates often describe data processing steps in isolation—'I normalized, filtered, ran PCA'—without connecting those choices to a real scientific question or explaining what the results told them about the biology or chemistry.