Biology PhDs with published papers

Mercor is seeking PhD-level life scientists with deep expertise across biology, omics, and computation to support a research project with a leading AI lab. In this role, you will leverage your domain knowledge in biology to create challenges for leading AI models - ensuring scientific accuracy, experimental rigor, and sound reasoning across complex biological questions. Your hands-on research experience will directly shape how next-generation AI systems understand and reason about the life sciences.

Create challenges related to molecular biology, genomics, transcriptomics, epigenetics, and/or proteomics with scientific accuracy and depth

Location Requirements

Role Overview

Key Responsibilities

Qualifications

Required

PhD

Currently or recently active in academia

Hands-on wet-lab experience

Strong experimental design and scientific reasoning

Bio-focused domain expertise

Evidence of research impact

Preferred

Computational and quantitative skills

Breadth across molecular modalities

Job Details

Mercor is seeking PhD-level life scientists with deep expertise across biology, omics, and computation to support a research project with a leading AI lab. In this role, you will leverage your domain knowledge in biology to create challenges for leading AI models - ensuring scientific accuracy, experimental rigor, and sound reasoning across complex biological questions. Your hands-on research experience will directly shape how next-generation AI systems understand and reason about the life sciences. Create challenges related to molecular biology, genomics, transcriptomics, epigenetics, and/or proteomics with scientific accuracy and depth Design experimental challenges, including evaluation of controls, confounders, reproducibility, and data interpretation Create high-quality training data including research-level questions, answers, and evaluation rubrics across domains Provide structured feedback on challenges covering wet-lab protocols, computational analysis pipelines, and study design Apply quantitative and bioinformatics reasoning to calibrate model performance on complex, multi-step biological problems PhD (or MD/PhD) in a life-science field such as molecular biology, biochemistry, genetics, bioengineering, or systems biology Currently or recently active in academia - e.g., faculty, staff scientist, research scientist, or postdoc with an active institutional affiliation and ongoing research Hands-on wet-lab experience - must have designed and executed experiments (not purely computational) Strong experimental design and scientific reasoning - demonstrated expertise with controls, confounders, reproducibility, and interpretation Bio-focused domain expertise in at least one of the following: Transcriptomics Epigenetics Proteomics / protein assays / protein biochemistry Evidence of research impact - published peer-reviewed work or influential preprints/tools/datasets with clear contributions (e.g., first/second author papers, senior-author leadership, or widely used methods and resources) Computational and quantitative skills - experience in computational biology, bioinformatics, statistical genetics, and/or machine learning for omics (analysis, interpretation, and study design support) Breadth across molecular modalities: DNA / RNA / protein biology; gene expression and translation Protein assays (e.g., Western blot, ELISA, mass spectrometry-based proteomics) Small molecules / chemical biology exposure We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

• Create challenges related to molecular biology, genomics, transcriptomics, epigenetics, and/or proteomics with scientific accuracy and depth
• Design experimental challenges, including evaluation of controls, confounders, reproducibility, and data interpretation
• Create high-quality training data including research-level questions, answers, and evaluation rubrics across domains
• Provide structured feedback on challenges covering wet-lab protocols, computational analysis pipelines, and study design
• Apply quantitative and bioinformatics reasoning to calibrate model performance on complex, multi-step biological problems
• PhD (or MD/PhD) in a life-science field such as molecular biology, biochemistry, genetics, bioengineering, or systems biology
• Currently or recently active in academia - e.g., faculty, staff scientist, research scientist, or postdoc with an active institutional affiliation and ongoing research
• Hands-on wet-lab experience - must have designed and executed experiments (not purely computational)
• Strong experimental design and scientific reasoning - demonstrated expertise with controls, confounders, reproducibility, and interpretation
• Bio-focused domain expertise in at least one of the following:

Genomics

Transcriptomics

Epigenetics

Proteomics / protein assays / protein biochemistry

• Genomics
• Transcriptomics
• Epigenetics
• Proteomics / protein assays / protein biochemistry
• Evidence of research impact - published peer-reviewed work or influential preprints/tools/datasets with clear contributions (e.g., first/second author papers, senior-author leadership, or widely used methods and resources)
• Genomics
• Transcriptomics
• Epigenetics
• Proteomics / protein assays / protein biochemistry
• Computational and quantitative skills - experience in computational biology, bioinformatics, statistical genetics, and/or machine learning for omics (analysis, interpretation, and study design support)
• Breadth across molecular modalities:

DNA / RNA / protein biology; gene expression and translation

Protein assays (e.g., Western blot, ELISA, mass spectrometry-based proteomics)

Small molecules / chemical biology exposure

• DNA / RNA / protein biology; gene expression and translation
• Protein assays (e.g., Western blot, ELISA, mass spectrometry-based proteomics)
• Small molecules / chemical biology exposure
• DNA / RNA / protein biology; gene expression and translation
• Protein assays (e.g., Western blot, ELISA, mass spectrometry-based proteomics)
• Small molecules / chemical biology exposure
• Pilot program to start. High touch and high excellence required - with potential for extension based on performance.