STEM Expert Chemistry and Computational Chemistry PhD

Evaluate and improve AI systems using chemistry and computational chemistry expertise. This part-time contract pays $45 to $90 per hour and requires a relevant PhD, strong English, and scientific software skills.

You will help evaluate and improve AI systems through chemistry problem-solving and technical review. The work covers both experimental and computational chemistry, with a focus on accurate reasoning, reproducible methods, and useful scientific recommendations.

• Design, evaluate, and critique chemistry and computational chemistry problems for AI benchmarking.
• Assess reaction mechanisms, synthesis planning, spectroscopy analysis, molecular simulations, and electronic structure methods.
• Review experimental protocols, simulation parameters, and scientific software configurations for accuracy and reproducibility.
• Analyze chemical, spectroscopic, and simulation data and provide actionable recommendations.
• Help develop and validate chemistry-related AI models and tools.
• Document methods, findings, and best practices for clear technical knowledge transfer.

This is a part-time contractor role for candidates with advanced chemistry expertise. The work may suit someone focused on experimental chemistry, computational research, or both.

• Pay: $45 to $90 per hour.
• Location:
  
  Open to candidates in the countries listed for this role, including the United States, Canada, the United Kingdom, India, Japan, Brazil, and other eligible locations.
• Education:
  
  PhD in chemistry, computational chemistry, chemical biology, materials science, or a closely related field.
• Research background:
  Hands-on wet-lab research experience for experimental work, or extensive simulation-focused research for computational work.
• Expertise:
  Deep knowledge in at least one chemistry specialty.
• Language:
  Professional written and spoken English at C1 level or higher.
• Tools:
  Proficiency in Python or scientific tools such as Gaussian, ORCA, Psi4, GROMACS, LAMMPS, or RDKit.
• Core skills:
  Quantitative analysis, scientific reasoning, documentation, and communication.
• Helpful background:
  Postdoctoral research, research scientist or faculty experience, peer-reviewed publications, patents, open-source scientific software, chemistry benchmarking with AI or machine learning, drug discovery, reaction prediction, or retrosynthesis.