R&D Systems & Lab Operations Specialist

About this role

Job Summary: 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.

Skills

Key responsibilities

• Review and refine rubrics to validate the accuracy and usability of laboratory processes within electronic lab environments.
• Translate experimental protocols into structured workflows using Electronic Lab Notebooks (ELNs) and Laboratory Information Management Systems (LIMS).
• Assess and enhance the clarity of documentation to ensure written protocols are actionable, logical, and ready for digital adoption.
• Collaborate with cross-functional teams to identify workflow improvements and ensure realistic QA pairs that mirror everyday lab operations.
• Monitor and track protocol execution, providing feedback to ensure compliance with best practices in digital lab management.
• Champion quality assurance by identifying gaps, inconsistencies, and opportunities for automation in lab processes.
• Serve as a key contributor to the development of scalable and reproducible lab data management strategies.

Required skills & qualifications

• Proven expertise with ELNs and LIMS, including workflow design and system configuration.
• Exceptional written and verbal communication skills to convey complex information clearly across diverse audiences.
• Strong analytical mindset and attention to detail in the review and optimization of digital lab processes.
• Experience translating laboratory protocols into structured, digital formats with robust traceability.
• Solid understanding of quality assurance principles in R&D settings.
• Ability to work independently and as part of a remote, expert-driven team.
• Demonstrated experience developing or reviewing rubrics for laboratory or scientific operations.

Preferred qualifications

• Advanced degree in life sciences, chemistry, engineering, or related discipline.
• Familiarity with automated data capture and digital transformation initiatives in laboratory environments.
• Previous experience as a reviewer or validator in a digital lab setting.