Computational Drug Discovery Design Jobs (NOW HIRING)

About Transcripta Bio
Transcripta Bio is a preclinical-stage AI drug discovery company pioneering a patient-first approach to therapeutics. Headquartered in Palo Alto, CA, we have built a proprietary closed-loop discovery engine - comprising our Disease Signature Atlas, Drug-Gene Atlas, and Conductor AI platform - that integrates single-cell patient transcriptomics, causal human genetics, and pre-validated chemistry to identify and advance drug candidates with a structural edge over conventional approaches.
We are looking for a Scientist to become a cornerstone of our drug discovery operations. You will own key areas of our experimental platform - from cell culture and high-throughput drug screening to the downstream assays that validate hits, interrogate mechanisms of action, and guide program decisions. This is a hands-on role with real scientific ownership, where your work directly powers our discovery engine.
What you'll do
• Maintain, expand, and bank disease-relevant human cell lines, including induced pluripotent stem cells (iPSCs) and iPSC-derived cell types, while ensuring consistent quality and reproducibility.
• Lead and support high-throughput small molecule drug screening campaigns utilizing automated liquid handlers and plate-based platforms. Design screening workflows, ensure data integrity, and resolve technical issues efficiently.
• Design and conduct downstream validation experiments to confirm screening hits and interrogate drug mechanisms of action, utilizing high-content imaging, qPCR, immunocytochemistry, Western blot, ELISA, and quantitative protein assays.
• Develop and optimize cell-based assays for disease-relevant biological readouts, collaborating with computational and therapeutic teams to align experimental outputs with platform requirements.
• Translate complex datasets into clear scientific narratives. Present findings at internal meetings and contribute to reports, publications, and external communications.
• Maintain detailed records in the electronic laboratory notebook (ELN) and contribute to SOPs, protocol documentation, and best practice development as the organization scales.
• Serve as a technical resource for junior team members, supporting a culture of scientific excellence.
• Support lab operations, including reagent preparation, equipment maintenance, and vendor coordination.
Qualifications
Required
• PhD in Cell Biology, Biochemistry, Molecular Biology, Pharmacology, or a closely related field with 3-5+ years of industry or postdoctoral experience; or MS with 6+ years of relevant industry experience.
• Demonstrated expertise in iPSC maintenance, differentiation, and quality assessment. Experience with primary human cells or disease-relevant iPSC-derived cell types is strongly preferred.
• Hands-on experience running or supporting high-throughput drug screening workflows, including familiarity with liquid handling automation (e.g., Hamilton, Tecan, Beckman, or equivalent).
• Relevant experience in small molecule drug discovery, including interrogating drug mechanism of action in cellular models.
• Proficiency in downstream validation techniques, including high-content imaging and analysis (e.g., Opera Phenix, ImageXpress), immunocytochemistry, Western blot, and quantitative protein assays (ELISA, MSD, or equivalent).
• Strong experimental design instincts: ability to independently scope assays, troubleshoot, and interpret data with scientific rigor and speed.
• Excellent organizational skills and high standards of documentation; comfortable working in an ELN-based environment.
• Collaborative and communicative - you thrive in cross-functional teams and can translate bench-level findings for computational colleagues and leadership alike.
• Thrives in a fast-paced, resource-constrained startup environment where adaptability and initiative are essential.
Preferred
• Experience with functional genomics approaches.
• Familiarity with transcriptomics methods (bulk or single-cell RNA-seq) or experience working with bioinformatics teams to interpret experimental data.
• Basic data analysis skills using Python, R, or similar tools.