Data-heavy chemistry/materials Science Expert

Data-heavy chemistry/materials Science Expert

Data-heavy chemistry/materials Science Expert

Role Type: Contractor

Location: Remote

Scope of Work

Deliver comprehensive, data-rich insights based on your experience in chemistry or materials science, especially where AI coding agents have been integral to your process.
Document real-world scenarios where you utilized AI agents for tasks such as large codebase navigation, challenging debugging, feature development, or architectural modifications.
Compose clear, detailed written accounts and participate in feedback sessions to elucidate your methodologies and the impact of AI tools on scientific problem-solving.
Evaluate the effectiveness and limitations of AI agents in technical workflows, providing actionable recommendations for improving AI systems in STEM contexts.
Collaborate remotely with micro1’s project team, sharing domain-specific expertise and facilitating knowledge transfer through written and verbal channels.
Contribute to the development of datasets and training materials that reflect authentic chemistry/materials science practice using AI code-generation tools.
Identify unique case studies or complex technical challenges where AI coding agents enhanced productivity or enabled new scientific outcomes.

Preferred Qualifications

Advanced degree (or equivalent professional experience) in chemistry, materials science, chemical engineering, or a closely related STEM field.
Proven proficiency with AI coding agents, including Codex, Claude Science, Claude Code, and Claude Cowork, specifically for generating code sequences in scientific environments.
Demonstrated ability to apply AI agents beyond code assistance — such as debugging intricate issues, building end-to-end features, and addressing ambiguous scientific problems.
Track record of contributing to projects involving large or complex codebases in computational chemistry, materials modeling, or laboratory automation.
Exceptional written and verbal communication skills, with a focus on clarity in technical documentation and knowledge sharing.
Experience in evaluating, adopting, or pioneering new workflows or methodologies using AI-powered tools within scientific research or industrial settings.
Strong analytical and critical-thinking capabilities, with the ability to articulate process decisions and results effectively for AI system training purposes.