Pharmaceutical R&D Domain Expert

What you would do

• Review pharmaceutical knowledge work and model outputs to identify gaps, flawed reasoning, and unsupported claims
• Write instruction specifications and golden solutions that explicitly define quality standards for pharmaceutical tasks
• Design evaluation sets and benchmarks reflecting realistic drug development work across discovery, translational and clinical phases
• Calibrate standards with research teams to keep evaluation criteria consistent and measurable
• Provide rigorous technical feedback grounded in industry experience and scientific principles

Who they want

• PhD or PharmD in medicinal chemistry, pharmacology, molecular biology, pharmaceutical sciences, toxicology or MD with substantive drug development experience
• 4+ years pharmaceutical or biotechnology R&D experience at a company, biotech firm, contract research organization or translational institute
• Deep specialization in one pipeline stage: target identification, medicinal chemistry, preclinical pharmacology, clinical development or regulatory affairs
• Clear progression to senior scientist, principal investigator, research director or VP R&D level with ownership of discovery projects or development programs
• Hands-on professional experience using large language models and judgment to evaluate sound reasoning versus plausible-sounding errors; must live in Bay Area

Main skills

What the interview asks about

1. 1.Spotting flawed pharmaceutical reasoning

   Expert judgment distinguishes scientifically sound approaches from plausible-sounding errors, crucial for guiding AI toward reliable drug development reasoning.

   For example: “An AI model proposes using a compound's logP value alone to predict oral bioavailability without considering permeability, metabolism or efflux. Explain why this reasoning fails and what additional factors matter.”

2. 2.Writing explicit quality specifications

   Translating tacit judgment into teachable criteria allows AI systems to learn genuine pharmaceutical logic.

   For example: “Define 3-4 specific criteria distinguishing a rigorous preclinical safety assessment from a superficial one. What evidence would you require in each case?”

3. 3.Designing realistic pharmaceutical benchmarks

   Benchmarks grounded in industry practice better evaluate whether AI can handle genuine drug development challenges.

   For example: “You're designing a benchmark for target identification in oncology. What specific decision points would you test? Include clinical context, molecular data and strategic considerations.”

4. 4.Calibrating standards across evaluators

   Consistent quality standards prevent evaluation drift and ensure reliable AI performance assessment.

   For example: “Two evaluators disagree on whether a medicinal chemistry proposal is adequately justified: one wants 'more supporting data', the other says it's 'speculative'. How would you make this standard explicit and measurable?”

A task you may get

Review 2-3 sample AI-generated pharmaceutical reasoning tasks. Identify flaws and gaps in at least one. Then write an instruction spec for a new drug development scenario and draft a golden solution demonstrating sound reasoning with calibration rubric.

How to prepare

• Compile 2-3 examples of drug development projects you owned: key technical decisions, data that informed choices, and why other approaches failed
• Document how you currently evaluate pharmaceutical reasoning: what makes one proposal strong versus weak in your specialty area
• Study recent pharma literature in your domain and identify 2-3 areas where AI reasoning often fails versus where it approximates expert judgment
• Draft a quality rubric for your pipeline stage: what distinguishes adequate from excellent pharmaceutical analysis

The facts

Pay

$75–115/hr

Hours

Full time, 40 hours a week

Where

Hybrid · Bay Area, CA

Open to

USA

Field

Life, Physical, and Social Science

Posted

8/19/2026

Places left

10

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