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

1. Demonstrate and document advanced workflows using AI coding agents—specifically Codex, Claude Science, Claude Code, and Claude Cowork—in real technical projects.
2. Analyze large codebases, debug complex technical issues, or develop novel features using AI agents, providing clear explanations of your process and decision-making.
3. Detail scenario-based use cases beyond basic coding assistance, such as making architectural changes, navigating ambiguous technical challenges, or generating sequential code outputs.
4. Submit high-quality written deliverables illustrating your use of AI agents in STEM contexts, including step-by-step breakdowns and outcomes.
5. Participate in virtual discussions or feedback sessions to share insights and approaches with project stakeholders.
6. Contribute structured input that aids in refining AI models’ reasoning and problem-solving capabilities based on your real-world experience.

Preferred Qualifications

1. Current STEM professional with hands-on experience applying AI coding agents within technical workflows.
2. Proficiency with Codex, Claude Science, Claude Code, and Claude Cowork, particularly for generating sequential outputs rather than chat-based interactions.
3. Demonstrated ability to solve complex, ambiguous, or open-ended technical problems utilizing AI agentics tools.
4. Exceptional written and verbal communication skills, with a strong focus on clarity and detail in technical documentation.
5. Experience advancing projects involving large codebases, debugging, feature development, or substantial code architecture changes aided by AI agents.
6. Ability to distill complex processes for AI training purposes, supporting the creation of educational or evaluative content for next-generation models.
7. Motivated to share real-world strategies and contribute to the evolution of AI-driven technical problem solving.

Originally posted on Himalayas