Quantitative Research Expert

Role Title: Quantitative Research Expert

Role Type: Contractor

Location: Remote

micro1 is engaging Quantitative Research Experts to contribute to a customer’s project focused on advancing real-world AI applications in STEM domains. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.

No prior experience in AI is required — your domain knowledge is what matters.

Scope of Work

• Demonstrate and document advanced workflows using AI coding agents—specifically Codex, Claude Science, Claude Code, and Claude Cowork—in real technical projects.

• Analyze large codebases, debug complex technical issues, or develop novel features using AI agents, providing clear explanations of your process and decision-making.

• Detail scenario-based use cases beyond basic coding assistance, such as making architectural changes, navigating ambiguous technical challenges, or generating sequential code outputs.

• Submit high-quality written deliverables illustrating your use of AI agents in STEM contexts, including step-by-step breakdowns and outcomes.

• Participate in virtual discussions or feedback sessions to share insights and approaches with project stakeholders.

• Contribute structured input that aids in refining AI models’ reasoning and problem-solving capabilities based on your real-world experience.

Preferred

Qualifications

• Current STEM professional with hands-on experience applying AI coding agents within technical workflows.

• Proficiency with Codex, Claude Science, Claude Code, and Claude Cowork, particularly for generating sequential outputs rather than chat-based interactions.

• Demonstrated ability to solve complex, ambiguous, or open-ended technical problems utilizing AI agentics tools.

• Exceptional written and verbal communication skills, with a strong focus on clarity and detail in technical documentation.

• Experience advancing projects involving large codebases, debugging, feature development, or substantial code architecture changes aided by AI agents.

• Ability to distill complex processes for AI training purposes, supporting the creation of educational or evaluative content for next-generation models.

• Motivated to share real-world strategies and contribute to the evolution of AI-driven technical problem solving.

Originally posted on Himalayas