Biology PhD - Scientific Coder

Mercor; Toronto; On-site; CAD 34,000 - 62,000; Part time

Job summary

Mercor is seeking PhD or Master’s scientists to author executable AI evaluation tasks for a new scientific benchmarking effort. You will design original problems, source materials, and craft strict grading criteria to challenge frontier models.

The role emphasizes biology-focused domains with a strong emphasis on Python, R, and reproducible workflows using Git/GitHub and Docker. This six-week engagement invites part-time contributors to start immediately.

Qualifications

• PhD in biology or related field.
• Demonstrated depth in ecology, biochemistry, or genetics (at least two).
• Proficiency in Python, R, or another scientific computing language.
• Comfortable with Git/GitHub and running code in Docker via PR workflows.

Responsibilities

• Source your own material (papers, datasets, repositories, or designed scenarios).
• Write scientific prompts based on input material.
• Build grading criteria to define correct answers.
• Calibrate prompts against frontier models so tasks fail more often than succeed.

Skills

Python

R

Scientific computing

Git/GitHub

Docker

Education

PhD in biology or related field

Master's degree considered

Tools

Docker

Git/GitHub

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