Biology PhD - Scientific Coder

Mercor; Greater London; On-site; GBP 55,000 - 83,000; Part time

Job summary

Mercor is seeking PhD and Master’s level scientists to author AI evaluation tasks (Sci Code). You will create original, executable research problems that frontier models cannot solve and contribute to a new benchmark for scientific computing.

Work involves sourcing materials, writing prompts, building grading criteria, and calibrating against frontier models. This six-week, part-time engagement starts immediately and offers a collaborative, research-focused environment in which your work

Qualifications

• PhD in biology or related field.
• 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 your own scenario.
• 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.

Skills

Python

R

Scientific computing

Education

PhD in biology

MSc in biology

Tools

Git/GitHub

Docker

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