Job Summary:

Join our customer’s team as a Molecular Biologist, specializing in CRISPR and gene editing, to advance cutting-edge projects and contribute to AI training initiatives. You will leverage your expertise to design, execute, and optimize molecular biology workflows, supporting innovative research and development efforts.

Key Responsibilities:

1. Lead the design, optimization, and execution of CRISPR-based gene editing experiments.
2. Develop and implement cloning strategies to generate novel constructs and cell lines.
3. Perform genotyping, PCR-based assays, and sequence analyses to validate genetic modifications.
4. Design synthetic DNA/RNA sequences and contribute to screening logic for high-throughput workflows.
5. Collaborate with interdisciplinary teams to translate experimental data into structured formats for AI model training.
6. Apply molecular biology techniques to study and characterize molecules, organelles, and cellular organization.
7. Document experimental procedures and results with exceptional clarity and precision, supporting reproducibility and knowledge transfer.

Required Skills and Qualifications:

1. Expertise in CRISPR technology and gene editing workflows.
2. Extensive hands-on experience with cloning, genotyping, and PCR-based assays.
3. Strong background in sequence design and molecular screening strategies.
4. Excellent written and verbal communication skills, with keen attention to scientific detail.
5. Ability to research and analyze molecular and organelle functions within cells.
6. Demonstrated commitment to data accuracy, workflow optimization, and experimental rigor.
7. Self-driven and collaborative, with a track record of contributing to high-impact research projects.
8. Bachelor’s degree or higher in Molecular Biology, Biology, or a related field.

Preferred Qualifications:

1. Experience with the development or evaluation of rubrics, especially for AI and data annotation workflows.
2. Familiarity with designing datasets or protocols for AI training and human review processes.
3. Previous collaboration with interdisciplinary or remote teams on data-driven biology projects.