R&D Systems & Lab Operations Manager

Description:

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.

Key Responsibilities:

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

Required Skills and Qualifications:

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

Preferred Qualifications:

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