Data-heavy chemistry/materials Science Expert
micro1
WorldwideremotePosted 18 days ago
Skill Required
Computational-Chemistry-Data-ScientistComputational-Materials-ScientistChemical-Data-ScienceMaterials-Science-ResearcherEngineeringanalyticalautomationdebuggingbuildingwrittenandAIContract
Key highlights
- Role Type: Contractor
- Location: Remote
- No prior experience in AI is required — domain knowledge is what matters
- Must have proficiency with specific AI coding agents (Codex, Claude Science, Claude Code, Claude Cowork)
- Experience with large or complex codebases in computational chemistry, materials modeling, or laboratory automation preferred
- Evaluated effectiveness and limitations of AI agents with actionable recommendations
Role overview
micro1 is engaging Data-heavy chemistry/materials Science Experts to contribute to a cutting-edge customer project centered on leveraging AI agents for scientific advancement. The role involves applying domain expertise to train next-generation AI systems by providing high-quality, real-world input that shapes how models learn, reason, and perform. This is an ideal opportunity for professionals with advanced chemistry or materials science knowledge who have integrated AI coding agents into their technical workflows, regardless of prior AI experience.
Responsibilities
- 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.
Requirements
- 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.
Additional details
- Role Title: Data-heavy chemistry/materials Science Expert
- Role Type: Contractor
- Location: Remote
- micro1 is engaging Data-heavy chemistry/materials Science Experts to contribute to a cutting-edge customer project centered on leveraging AI agents for scientific advancement.
- No prior experience in AI is required — your domain knowledge is what matters.
- This opportunity is ideal for professionals who have integrated advanced AI coding agents, such as Codex, Claude Science, Claude Code, and Claude Cowork, into their technical workflows for complex chemistry or materials science initiatives.