Remote AI Researcher (PhD/Master) - Shape Reasoning

Alignerr; Cambridge; Remote; GBP 61,000 - 123,000; Part time

Job summary

Alignerr is seeking Masters and PhD-level researchers for AI training projects. This fully remote, hourly contract role offers flexible hours (10–40 hrs/week) and the chance to work with world-leading AI labs on cutting-edge model development.

You will design and solve complex, multi-disciplinary problems, review AI outputs for rigor, and collaborate asynchronously with researchers to improve model performance, all while maintaining high standards of clarity and integrity.

Qualifications

• Must hold a Master’s or PhD in a STEM or highly technical field.
• Proven track record in research, publications, or complex technical projects.
• Exceptional written communicator with clarity and precision.
• Detail-oriented with a rigorous approach to problem-solving.
• Self-directed and comfortable working asynchronously.
• Based in the US, Canada, UK, Australia, or New Zealand.

Responsibilities

• Design and solve advanced, multi-disciplinary problems across your domain of expertise.
• Apply your research background to craft complex problem statements that challenge AI reasoning.
• Review and evaluate AI-generated outputs for academic rigor, accuracy, and depth.
• Collaborate asynchronously with AI researchers and domain experts to improve model performance.
• Ensure the highest standards of clarity, precision, and intellectual integrity across all deliverables.

Skills

Masters/PhD level research

Research publications

Academic writing

Independent/asynchronous work

Strong written communication

Education

Master's degree

PhD

Tools

Python

R

Job description

Alignerr is seeking Masters and PhD-level researchers for AI training projects. This fully remote, hourly contract role offers flexible hours (10–40 hrs/week) and the chance to work with world-leading AI labs on cutting-edge model development.

You will design and solve complex, multi-disciplinary problems, review AI outputs for rigor, and collaborate asynchronously with researchers to improve model performance, all while maintaining high standards of clarity and integrity.