Bioinformatics Scientist - Gene Regulation & Transformer Modeling

Bioinformatics Scientist - Gene Regulation & Transformer Modeling

About us

e184 is a biotechnology research company advancing in vitro gametogenesis to transform reproductive medicine. We're developing integrated platforms that combine cellular reprogramming, machine learning-guided optimization, multi-omics analysis, and automated experimental workflows to enable gamete development for individuals facing reproductive challenges. We're assembling interdisciplinary teams across cell and molecular engineering, synthetic biology, epigenetic editing, bioinformatics and computational biology to tackle one of biology's most impactful problems - returning the fundamental right to procreate.

Role overview

As a Bioinformatics Scientist specializing in genomic foundation models, you will lead computational analysis of multi-modal genomics data (scRNA-seq, ATAC-seq) to identify transcription factor combinations driving desired cell state conversion. This role bridges classical gene regulatory network inference and modern foundation model approaches, requiring deep expertise in single-cell genomics analysis, transcriptional regulation biology, and demonstrated interest in applying transformer-based methods to cellular reprogramming problems. You will work on traditional multi-platform genomics analysis and on integrating and fine-tuning foundation models, collaborating closely with wet lab teams to translate computational predictions into experimental designs for our cell fate engineering platform.

Primary responsibilities

• Perform genomics analysis across scRNA-seq and ATAC-seq data from human, NHP, and mouse gametogenesis to identify transcription factors governing cell state trajectory
• Integrate classical and modern approaches by combining GRN inference methods with transformer-based models to create hybrid TF ranking systems, leveraging both motif-guided statistical learning and self-supervised deep learning representations
• Build version-controlled data platforms harmonizing gametogenesis datasets across multiple modalities and sequencing platforms, preparing integrated datasets for both traditional analysis and foundation model fine-tuning
• Develop trajectory inference workflows using RNA velocity, pseudotime analysis, and optimal transport models to map cellular transitions, identifying critical commitment points where TF interventions are most effective
• Apply chromatin accessibility analysis and TF motif enrichment to decode regulatory grammar at key transition junctions, identifying synergistic TF combinations and repressive factors required for cell fate conversion
• Collaborate with experimental teams on screen design and interpretation, translating computational predictions into biologically interpretable experimental plans specifying which TF combinations to test and validation strategies
• Establish automated feedback loops for ingestion of screening NGS data, implementing active learning strategies that prioritize informative experiments and building retraining pipelines that continuously improve prediction accuracy
• Build computational infrastructure for reproducible bioinformatics workflows and foundation model fine-tuning

Required qualifications

Preferred qualifications

What we offer

Disclaimer

The above job description is intended to describe the general nature and level of work being performed by individuals assigned to this position. It is not intended to be an exhaustive list of all duties, responsibilities, and skills required. Responsibilities and duties may change or be adjusted to meet the needs of the company, and additional duties may be assigned as necessary. The job description is subject to change at any time at the discretion of e184.

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