Pharmacology Expert - Fully Remote | Up To $100/Hr Part-Time

Mercor; San Francisco (CA); Hybrid; GBP 72,000 - 103,000; Part time

Job summary

Mercor connects elite creative and technical talent with leading AI research labs. The Drug Discovery Specialist, Pharmacology & ADME/Tox, is a contract, remote role offered to an experienced scientist who can dedicate 20+ hours per week during weekdays.

You will guide research teams on mechanism of action, ADME/Tox, PK/PD and data-driven drug discovery tasks, develop evaluation criteria, and provide clear feedback on model outputs and experimental designs.

Qualifications

• PhD in pharmacology, pharmaceutical sciences, toxicology, DMPK, biochemistry, or related field.
• 3+ years in early-to-mid stage drug discovery, pharmacology, DMPK/ADME, or toxicology.
• Hands-on experience analyzing experimental drug discovery data (ADME, PK/PD, CRO readouts, LC-MS).
• Familiar with PK/PD or data analysis software and scientific AI tools.
• Available for at least 20 hours/week on weekdays.
• Strong written communication and ability to explain complex decisions.

Responsibilities

• Guide research and engineering teams to improve AI model performance in pharmacology, ADME/Tox, PK/PD.
• Design drug discovery tasks, benchmarks, and training data; write accurate solutions.
• Evaluate AI outputs and diagnose errors in scientific logic, tool usage, and factual accuracy.
• Develop specifications, grading rubrics, and verification processes for drug discovery data produced by external teams.
• Advise on assay design and quality control, failure modes, and biological validity of model outputs.
• Collaborate with SMEs to ensure consistency and accuracy in training data.

Skills

PK/PD

ADME

Data analysis

AI tools

Scientific writing

Independent work

Education

PhD in pharmacology, pharmaceutical sciences, toxicology, DMPK, biochemistry, or related

Tools

PK/PD software

LC-MS data analysis

CRO readouts

Bioanalysis

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Drug Discovery Specialist, Pharmacology & ADME/Tox
Type: Contract
Compensation: $70–$100/hour
Location: Remote

Role Responsibilities

• Guide research and engineering teams to close knowledge gaps and improve AI model performance in mechanism of action, target and cellular pharmacology, ADME/Tox, and PK/PD.
• Design challenging, non-searchable drug discovery tasks, benchmarks, and training data. Write accurate, well-structured solutions to problems such as interpreting assay readouts, identifying compound liabilities, and planning optimization strategy.
• Evaluate AI model outputs and step-by-step reasoning on drug discovery tasks. Diagnose errors in scientific logic, tool usage, and factual accuracy. Provide clear, written technical feedback.
• Develop specifications, grading rubrics, and verification processes for drug discovery data produced by external technical teams. Act as the domain authority on data quality.
• Advise on assay design and quality control, experimental failure modes, and the biological and pharmacological validity of model outputs.
• Collaborate with other subject matter experts to ensure consistency and accuracy in training data.

Qualifications

Must-Have

• PhD in pharmacology, pharmaceutical sciences, toxicology, DMPK, biochemistry, or a closely related discipline, or equivalent research experience.
• 3+ years of experience in early-to-mid stage drug discovery, pharmacology, DMPK / ADME, or toxicology at a recognized organization.
• Hands-on experience analyzing experimental drug discovery data, such as in vitro ADME and safety assays, PK/PD studies, CRO assay readouts, or LC-MS bioanalysis.
• Comfortable using PK/PD or data analysis software and scientific AI tools.
• Ability to engage reliably for at least 20 hours/week during weekdays.
• Strong written communication skills and the ability to explain complex technical decisions clearly.

Preferred

• Experience with PK/PD or ADME modeling, writing grading rubrics, designing benchmark tasks, or evaluating scientific AI models.