Computational Scientist; Proteomics

We are seeking an experienced Computational Scientist with expertise in mass spectrometry and omics research to help improve the scientific reliability of AI-powered analytical systems.

You will apply your domain knowledge to develop evaluation tasks, curate datasets, and assess whether AI-generated interpretations are scientifically sound. Working with engineering and research teams, you will help establish rigorous standards for AI reasoning across proteomics.

This is a remote position

Salary range: $120K or higher

Flexible hours

• Develop scientific benchmarks:
  Create realistic analytical challenges and expert reference answers that test AI’s ability to interpret complex experimental data.
• Evaluate scientific validity:
  Distinguish genuine biological findings from technical artifacts, contamination, batch effects, and processing errors.
• Adjudicate ambiguous results:
  Assess evidence, identify analytical limitations, and recommend follow-up experiments where needed.
• Improve AI performance:
  Analyze model reasoning, identify failure modes, and evaluate the accuracy, scientific rigor, and calibration of confidence.
• Collaborate across disciplines:
  Work with engineers and scientists to translate domain expertise into scalable evaluation frameworks and workflows.
• Curate research datasets:
  Support benchmark development across proteomics, lipidomics, metabolomics, and relevant complementary biological data types.

• PhD in proteomics, analytical chemistry, biochemistry, or a related discipline.
• Hands-on mass spectrometry experience, including experimental data analysis and a strong publication record.
• Strong understanding of experimental design, statistics, analytical quality control, and biological interpretation.
• Ability to identify technical artifacts, challenge unsupported conclusions, and communicate evidence-based scientific reasoning.
• Practical experience with AI assistants or AI-enabled development tools, such as Claude, Cursor, or similar platforms.

• Experience in lipidomics or metabolomics beyond proteomics.
• Familiarity with protein sequencing, antibody-based assays, or sequencing technologies.
• Experience with scientific validation, benchmark development, or computational research workflows.

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