Drug Discovery Scientist (Remote | $60–$90/hr)

Synthires

United States

Teletrabalho

USD 83 000 - 124 000

Tempo parcial

Há 4 dias
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Vantagens oferecidas por esta oferta de emprego

Fully remote
Claude Max subscription reimbursed
Hourly contract engagement

Resumo da oferta

Synthires is seeking experienced Drug Discovery Scientists to contribute to an AI research and evaluation project focused on pharmaceutical R&D. You will design evidence-driven problems, build source-grounded data rooms, and craft rigorous grading rubrics.

This role emphasizes distinguishing genuine scientific reasoning from pattern matching and requires remote work with flexible hours. Your expertise in enzymology, structure-based design, and medicinal chemistry will guide the evaluation

Qualificações

  • PhD or equivalent industry experience in drug discovery or related field.
  • Proven ability to construct complex scientific problems with defensible conclusions.
  • Strong analytical and critical-thinking skills.
  • Experience evaluating AI-generated scientific outputs is a plus.

Responsabilidades

  • Design challenging, realistic drug discovery research problems based on authentic workflows.
  • Create scenarios where conclusions come from multiple pieces of evidence.
  • Build data rooms using real, source-grounded scientific data and documentation.
  • Ensure each evidence piece can be traced to a reliable source.
  • Develop grading documents and evaluation rubrics for reasoning quality.
  • Evaluate AI-generated research outputs for accuracy and defensibility.
  • Run verification and calibration to ensure reproducibility and difficulty.
  • Identify weaknesses and revise task designs accordingly.
  • Provide expert judgment on scientific questions and task quality.
  • Collaborate with reviewers and domain experts to refine tasks.

Conhecimentos

Mechanistic enzymology
Structure-guided peptide design
Structure-guided antibody design
Fragment-based drug discovery
Structure-based medicinal chemistry
Strong analytical and criticalthinking
Scientific writing and documentation
Remote work capability

Formação académica

PhD or equivalent industry experience in drug discovery, medicinal chemistry, chemical biology, biochemistry, or a closely related field

Ferramentas

Open-source scientific tooling
Computational research workflows

Descrição da oferta de emprego

This opportunity is for experienced Drug Discovery Scientists to contribute to an advanced AI research and evaluation project focused on pharmaceutical research and development.

You’ll apply your scientific expertise to design realistic, evidence-driven drug discovery problems that require AI models to reason through incomplete, indirect, and potentially misleading scientific evidence. You’ll create research prompts, build supporting data rooms using real source-grounded information, develop rigorous grading frameworks, and validate the resulting tasks.

The role is ideal for experts who can distinguish genuine scientific reasoning from pattern matching and confidently evaluate the quality of complex research conclusions.

Responsibilities
  • Design challenging, realistic drug discovery research problems based on authentic scientific workflows.
  • Develop research scenarios where the correct conclusion must be derived from multiple pieces of evidence rather than directly stated.
  • Build comprehensive data rooms using real, source-grounded scientific data and documentation.
  • Ensure every decisive scientific value and piece of evidence can be traced back to a reliable source.
  • Create detailed grading documents and evaluation rubrics that assess scientific reasoning and substantive conclusions.
  • Evaluate AI-generated research outputs for scientific accuracy, logical consistency, and defensibility.
  • Run verification and calibration processes to ensure tasks are reproducible, appropriately difficult, and objectively scorable.
  • Identify weaknesses in task design, evidence, or evaluation criteria and revise them accordingly.
  • Provide expert judgment on scientific questions, task difficulty, and the quality of AI reasoning.
  • Collaborate with project reviewers and other domain experts to refine research tasks.
Required Qualifications
  • Professional expertise in one or more of the following areas:
  • Mechanistic enzymology
  • Structure-guided peptide design
  • Structure-guided antibody design
  • Fragment-based drug discovery
  • Structure-based medicinal chemistry
  • PhD or equivalent industry experience in drug discovery, medicinal chemistry, chemical biology, biochemistry, or a closely related field.
  • Strong understanding of drug discovery research methodologies and scientific evidence evaluation.
  • Ability to construct complex scientific problems with defensible conclusions.
  • Strong analytical and critical-thinking skills.
  • Ability to evaluate scientific reasoning quality, not simply final answers.
  • Strong scientific writing and documentation skills.
  • Comfortable working independently in a remote research environment.
  • Ability to commit at least 20 hours per week.
Preferred Qualifications
  • Experience designing or evaluating drug discovery research strategies.
  • Strong background in structure-based drug design, protein-ligand interactions, enzymology, or molecular discovery.
  • Experience working with scientific literature, experimental datasets, structural data, and research documentation.
  • Experience developing research assessments, scientific benchmarks, or evaluation frameworks.
  • Familiarity with open-source scientific tooling and computational research workflows.
  • Experience evaluating AI-generated scientific research or technical outputs is a plus.
  • Strong ability to distinguish defensible scientific conclusions from unsupported assumptions or superficial reasoning.
Compensation
  • Competitive compensation of $60-$90/hour.
  • Hourly contract engagement.
  • Fully remote.
  • Minimum commitment: 20 hours/week.
  • Up to 40 hours/week available.
  • Claude Max subscription required for the project and fully reimbursed.
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