Pharmacology training data, checked by pharmacists.
A dropped unit, a look-alike name, a renal adjustment nobody made: these are the errors that make drug AI dangerous, and the ones a pharmacist catches instantly. eeko commissions licensed pharmacists, pharmacologists, and toxicologists to write and grade the drug data your model learns from. Every item fingerprinted, second-checked, and licensed.
What is pharmacology training data?
Pharmacology training data is the drug knowledge a model learns from and is tested against: a dosing case with the arithmetic written out and the units a correct answer must carry, an interaction scenario with mechanism, severity, and what a pharmacist would do about it, a PK/PD problem with its solution, a drug label broken into structured fields, a model answer graded by someone who dispenses for a living.
Language models handle this unevenly. They confuse names that look alike, drop the unit from a dose, forget that kidneys and livers change the math, miss the interaction with something bought off a pharmacy shelf, or repeat guidance that was withdrawn years ago. A general annotator reads past all of it. So eeko's data practice commissions credential-checked pharmacists and pharmacologists to write items that target exactly those points, and has a second expert confirm each one.
This page belongs to eeko's human-made AI training data practice, built because the clinical systems we deliver for providers and pharma (see our healthcare AI work) need data nobody has published. See also training data for life sciences and how eeko verifies human data.
Where drug models go wrong, and what fixes it.
Three failure patterns from medication-safety evaluations, and the data decision behind each fix.
Pharmacist annotation and drug datasets, to your spec.
Six request types we fill for medical and pharmacy AI teams, each one starting with a spec, then a pilot, at a fixed per-item price.
- Therapeutic area and setting to order
- Built for supervised fine-tuning data
- Includes common OTC and supplement cases
- Severity scale agreed with your team
- Authored by PhD pharmacologists
- See AI evaluation benchmarks
- Rubric scores and pairwise rankings
- Feeds RLHF preference data
- Schema drafted with your team
- Part of our document datasets
- Harmful-instruction screening on every item
- Extends red-teaming and safety data
From written spec to signed delivery.
Pilot before volume, fixed price per item, and a pharmacist on both ends of every item.
Every drug item, provable.
Medication AI gets audited by safety and regulatory teams. The data behind it should survive the audit.
What travels with each item
- Fingerprint — a cryptographic hash taken on arrival and compared across the set for duplicates.
- Signed provenance — who authored it, whether a model proposed any part, and which expert confirmed it.
- Independent timestamp — proof of when the item existed, from a source we do not control.
- Consent and licence — recorded per item, so you can show what each case may be used for.
- Safety and origin checks — harmful-instruction screening, plus generator signatures and C2PA or IPTC fields detected.
- Audit trail and receipt — hash-chained history, and a signed receipt naming every item in the batch.
It is the same discipline behind our own compliance and governance work.
Paid remote AI work for pharmacists and pharmacologists.
Work in the therapeutic area you already practice or research in, from anywhere, with the rate on the job before you take it. No dispensing, no counseling, nothing published under your name.
The work itself
- Write dosing and interaction cases — realistic scenarios with the correct reasoning, the calculation, and the action a pharmacist would take.
- Grade model answers — score responses on accuracy, units, safety, and referral using a rubric, and note each deduction.
- Structure drug labels — turn the indications, warnings, and dosing tables in a label into the fields of a defined schema.
- Second-review a peer — check another expert's cases and answers before the batch is delivered.
Jobs come to you when your verified area fits, and every one states its scope, deadline, and rate. Start at apply as an expert, or read how the program works on expert data annotation jobs.
Identity and credentials are confirmed once, before any job is offered. After that, a peer reviews your work, not a generalist QA team.
- Degree PharmD, BPharm, or equivalent
- Licence Pharmacist registration held now or recently
- Doctorate Doctorate in pharmacology, toxicology, or an allied science
- Practice Years in clinical, hospital, community, or research settings
- Specialty Oncology, critical care, and other board-certified areas welcome
You see the rate and the scope, then decide. Approved work is credited and paid out with an itemized record.
- Transparent Rate on every job before you accept
- Remote Your schedule, inside the deadline
- Reviewed Second expert approves, payment releases
- Private Never named on delivered data
Related fields, solutions, and industries.
Pharmacology training data questions.
What is pharmacology training data?
Pharmacology training data is the drug knowledge a model learns from and is tested against: dosing cases with the calculation written out, interaction scenarios with mechanism and management, PK/PD problems with solutions, structured fields pulled from labeling, and model answers graded by someone who dispenses for a living. At eeko it is written and reviewed by licensed pharmacists and pharmacologists, and every item ships with a fingerprint, a provenance record, and a licence.
Does this work count as dispensing or counseling patients?
No. You are writing and checking data for software. You do not dispense, counsel, or advise any patient or member of the public, and no pharmacist-patient relationship is formed. Nothing you write is handed to a person as your advice. Every job says up front what the data is for, and your name is never attached to delivered data.
Do I need a pharmacist license to join?
Most clinical pharmacy jobs ask for a pharmacy degree and a license held now or recently, which we confirm together with your identity before offering work. Research-flavored work, for example PK/PD or mechanism problems, may ask for a doctorate in pharmacology or toxicology instead. Every listing states the credentials it needs, and offers only reach you when your verified profile fits.
How are pharmacists paid for AI training work?
Every job states its rate before you take it, and nothing obliges you to take a minimum number. The work is remote, fits around clinical or research hours, and runs inside the deadline the job sets. Once a second expert approves your items they are credited to you and paid out, with an itemized record showing what was approved. Identity and tax details are confirmed once, at onboarding.
How much does pharmacist-written data cost?
Pricing is per item and depends on the therapeutic area, how much calculation or reasoning each item carries, and the depth of second-expert review required. A single interaction label costs far less than a renal dosing case with a worked solution and a rubric. We quote a fixed price for a pilot batch after reading your brief, so you can judge the standard and the format before scaling. Exclusive licences are priced separately.
Can we license the pharmacology dataset exclusively?
Yes. Commissioned sets can be licensed to you alone, so the same cases and evaluations are never offered elsewhere. Scope, term, and exclusivity are written down before any item is created. Batches ship in JSON Lines, CSV, Parquet, or Croissant, each with a signed receipt naming every item, and a standing order can keep new items arriving as guidance changes.
Drug data your safety team can sign off on.
Send the therapeutic area, the item types, and the format. A real person replies inside one business day with questions and a pilot price. Pharmacists can apply in minutes. No deck. A ranked list.
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Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: