Remote PK/PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)Mercor • Perris, California, US

Remote PK / PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)

Mercor • Perris, California, US

1 day ago

Job type

Part-time

Remote

Job description

This person complements the client’s “Translational / Clinical Pharmacology Decision-Maker” team by grounding dose selection and exposure–response analysis in

quantitative structure and parameter plausibility

###

Who we’re looking for

Deep hands-on experience in

PK, PD, exposure–response modeling

, and ideally

population PK or QSP

. - Expert at model fitting, sensitivity analysis, and identifying non-plausible parameter spaces. - Can evaluate the validity of dose–exposure predictions and detect high-risk extrapolations. - Comfortable designing

model evaluation rubrics

that distinguish between acceptable vs. non-credible outputs. - Able to articulate how quantitative checks should complement narrative decision logic.

Nice-to-have :

Experience supporting translational or clinical pharmacology leads in dose justification. - Familiarity with integrating nonclinical PK / PD data (2-species GLP → human FIH extrapolation). ###

Experience level

~8–12 years of quantitative pharmacology experience in

pharma, CROs, or modeling consultancies

. - Strong portfolio in

population PK / PD

exposure–response

, and

parameter estimation

using NONMEM, Monolix, or equivalent tools. - Demonstrated ability to interpret model results for decision-making, not just fit data. - Can create

fit-for-purpose models

and critique model structures or assumptions under uncertainty. ###

Expectations

Design and refine

micro-evaluations

for PK / PD performance (curve fits, parameter checks, error taxonomies). - Encode

quantitative sanity checks

into model rubrics for automated evaluation. - Define

failure conditions

(e.g., unsafe extrapolation, poor coverage curves, invalid assumptions).

Inputs we give :

PK / PD datasets, tox summaries, and performance prompts (e.g., “fit exposure–response curves, interpret safety margins”). - Example model outputs from automated systems.

Expected outputs :

Quantitative Rubrics :

clear thresholds for acceptable parameter fits, coverage curve quality, and model integrity checks. -

Golden Fit Examples :

representative “ideal” PK / PD model outputs and visualizations for calibration. -

Error Taxonomy :

structured list of typical modeling or fitting errors, with root-cause annotations. -

Meta-Layer Commentary :

short note per rubric capturing how expert modelers recognize implausible or unsafe fits beyond numeric error values. ###

Engagement Model & Compensation

Contract / part-time

, remote, outcome-based deliverables.

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