Shape and execute the company’s RNA and TF-based strategy to accelerate iPSC differentiation into germline fates.
Start hands-on in the lab and lead a small team, quickly taking on direct reports and mentoring scientists and research associates.
Design and lead transcription factor and gene perturbation projects, emphasizing RNA-based, non-integrative strategies.
Develop a company strategy for applying a clinically viable gene perturbation approach to speed up differentiation across multiple pipeline areas.
Drive innovation in non-integrative gene expression systems (mRNA, circRNA, CRISPRa/dCas9) and adapt these technologies to high-throughput screens.
Develop and apply high-throughput pooled TF/gene screening approaches (e.g., TFome, ORF libraries).
Integrate machine learning–guided or computational pipelines (e.g., ATAC-seq, DNase-seq, RNA-seq–driven predictions) with experimental design.
Operate with high ownership and autonomy, delivering rigorous results quickly to impact the company’s mission.
Requirements
PhD in Synthetic Biology, Developmental Biology, Stem Cell Biology, RNA Biology, or related field.
Strong publication record demonstrating impact in cell fate engineering, stem cell or developmental biology, transcription factor biology, or RNA biology.
Hands-on expertise with transcription factor–guided differentiation and gene perturbation approaches.
Experience designing and executing high-throughput genetic, ideally RNA-based screens.
Expertise with non-integrative gene modulation tools (mRNA, circRNA, dCAS/CRISPRa systems).
Familiarity with computational and/or machine learning approaches for TF or target discovery (via direct work or collaborations).
Demonstrated leadership in academia (e.g., leading large collaborations) or biotech (e.g., managing small teams).
Desire to thrive in a fast-paced, high-ownership startup environment.
Preferred: Experience with pooled ORF/TFome libraries, combinatorial TF perturbation, or barcoded screening workflows.
Preferred: Experience integrating chromatin accessibility data (ATAC-seq, DNase-seq) or transcriptomics into TF discovery.
Preferred: Prior management of small teams in academia or industry.