Preclinical R&D Expert – In-house Drug Development

What you would do

• Review scientific materials and experimental datasets from drug development programs you have worked on
• Classify and annotate research data to train AI models on pharmaceutical R&D patterns and quality
• Evaluate how computational predictions align with real preclinical biology and development constraints
• Participate in discussions about which therapeutic targets and modalities warrant further investment
• Help establish data quality standards and documentation practices for the AI platform's learning pipeline

Who they want

• Demonstrated hands-on experience in preclinical research at organizations building their own therapeutic programs through IND-enabling stages
• Core competency in at least one area: medicinal chemistry, discovery biology, protein design, toxicology, pharmacology, or formulation science
• Working knowledge of multiple drug platforms: small molecules, monoclonal antibodies, peptides, cell therapies, or gene therapy vectors
• Background leading programs from target selection through lead optimization or preclinical development milestones
• Availability for part-time engagement of roughly ten hours per week for ongoing consultations

Main skills

What the interview asks about

1. 1.Data annotation rigor

   The AI models depend on consistently labeled training data; interviewers need to understand whether candidates can maintain scientific precision while scaling annotation efforts.

   For example: “Describe your approach to annotating a compound's activity data when potency results varied between labs using different protocols - what metadata would you capture and why?”

2. 2.Modality selection rationale

   Choosing between therapeutic approaches shapes the entire development timeline and budget; this tests whether candidates make evidence-based recommendations or favor familiar paths.

   For example: “Your client is considering whether to pursue small molecules or antibodies for their target. What specific properties of the target would push you toward each, and what data gaps would you flag?”

3. 3.Model output interpretation

   Computational predictions can be seductively precise but scientifically nonsensical; evaluating candidates' ability to spot these errors protects clients from following bad recommendations.

   For example: “An AI model predicts a compound has excellent oral bioavailability based on properties, but you know the target class has chronic solubility issues. How do you assess that prediction?”

4. 4.Development risk assessment

   Early programs encounter toxicology surprises, manufacturing challenges, and biological unknowns; this measures whether candidates integrate realistic risk thinking into their scientific judgment.

   For example: “You have identified a promising lead compound, but it has structural features similar to compounds that caused liver toxicity in prior programs. How would you advise the team on next steps?”

5. 5.Cross-functional science translation

   The role bridges laboratory scientists and machine learning engineers with different vocabularies; interviewers assess whether candidates explain scientific concepts clearly to non-specialist audiences.

   For example: “A data scientist is confused about why two experiments on the same compound gave different ADME results. How would you explain the variance and what standardization would you recommend?”

A task you may get

Present a preclinical dataset from a published study, identify 3-5 key properties the AI platform should learn to predict, and explain why those properties matter for development decision-making.

How to prepare

• Review recent advances in AI applications to drug discovery and identify 2-3 specific limitations of current computational approaches
• Prepare a detailed case study from your own R&D experience highlighting a major decision and the evidence that drove it
• List the drug modalities and therapeutic areas where you have direct hands-on experience, with specific examples of candidates or programs you evaluated

The facts

Pay

$60–100/hr

Hours

Hourly, 40 hours a week

Where

Remote

Field

Life, Physical, and Social Science

Posted

8/15/2026

Places left

10

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