Biology PhD - Scientific Coder

Obsidian

Sydney

On-site

AUD 83,000 - 124,000

Part time

14 days+
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Job summary

Mercor is seeking PhD and Master's level scientists to author AI evaluation tasks for a new benchmark in scientific computing. You will craft original, executable research problems that current frontier models cannot solve and calibrate grading criteria to distinguish correct answers from incorrect ones.

The role emphasizes sourcing materials, writing prompts, and validating tasks through a pull-request workflow with automated quality checks.

Qualifications

  • PhD in biology, biological sciences, biochemistry, genetics, ecology, or closely related field.
  • Demonstrated depth in at least two subdomains: ecology, biochemistry, genetics.
  • Working proficiency in Python, R, or another language for scientific computing.
  • Comfortable with Git/GitHub and running code in Docker — PR workflow with automated checks.

Responsibilities

  • Source your own material: a published paper, a Kaggle dataset, an open-source repository, or a designed scenario.
  • Write scientific prompts based on the input.
  • Build grading criteria that define a correct answer.
  • Calibrate against frontier models — ensure task fails more often than it succeeds.

Skills

Python
R
Git/GitHub
Docker
Biology
Research

Education

PhD in biology or related field

Tools

Docker
Git

Job description

Mercor is hiring PhD and Master's scientists to author AI evaluation tasks (Sci Code)

Mercor is partnering with leading AI labs on a new benchmark for scientific computing. You will author original, executable research problems that today's frontier models cannot solve.

Domains — depth required in at least two subdomains (with a coding focus)
  • Biology — ecology, biochemistry, genetics
What you'll do
  • Source your own material: a published paper, a Kaggle dataset, an open-source repository, or a scenario you design
  • Write scientific prompts based on the input
  • Build the grading criteria that define a correct answer
  • Calibrate against frontier models — a task ships only when strong models fail it more often than they succeed
Required
  • PhD in biology, biological sciences, biochemistry, genetics, ecology, or a closely related field
  • Demonstrated depth in at least two of the following subdomains: ecology, biochemistry, genetics
  • Working proficiency in Python, R, or another relevant programming language for scientific computing
  • Comfortable with Git/GitHub and running code in Docker — authoring runs through a pull-request workflow with automated quality checks
Preferred
  • Publications in peer-reviewed journals
  • Prior scientific software or research engineering experience
Engagement
  • Duration: 6 weeks
  • Commitment: part-time, 20+ hours per week
  • Start date: immediate
Process
  1. A 25-minute conversational interview covering your background, experience, and motivations
  2. Follow up within a few days with next steps and onboarding
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