Bioinformatics Genomics Problem Designer - Single-Cell

Mercor

Greater London

On-site

GBP 70,000 - 100,000

Full time

14 days+

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Job summary

Mercor is seeking a Bioinformatics & Computational Single-Cell Genomics Expert to design graduate-level problems for evaluating AI systems using real scientific workflows. You will develop tasks that require using specialized tools to run simulations, interpret results, and design experiments.

The role involves crafting challenging, multi-step problem sets and refining them through iterative testing against AI models.

Qualifications

  • Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience)
  • Proven proficiency with at least one of the listed scientific software libraries, shown through research publications, open-source contributions, or professional work
  • Strong Python skills — you'll be writing problem setups, oracle functions, and solution validators
  • Ability to work independently and refine problem designs based on feedback
  • Comfortable working in a Linux/terminal environment with remote compute sandboxes
  • Available for at least 15–20 hours per week

Responsibilities

  • Design original, graduate-level problems based on real scientific workflows.
  • Test problems against state-of-the-art AI models and refine them to target difficulty.
  • Create problems requiring use of specialized scientific software.
  • Plan sequences of queries or experiments to reveal non-obvious information.
  • Ensure problem sets are robust and reproducible across environments.

Skills

Python
Independent work
Linux

Education

MS/PhD in STEM

Tools

scanpy
scvelo
squidpy
gudhi

Job description

Mercor is seeking a Bioinformatics & Computational Single-Cell Genomics Expert to design graduate-level problems for evaluating AI systems using real scientific workflows. You will develop tasks that require using specialized tools to run simulations, interpret results, and design experiments.

The role involves crafting challenging, multi-step problem sets and refining them through iterative testing against AI models.

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