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

• PhD in Bioinformatics, Computational Biology, or related quantitative field (or MS with 5 years relevant industry experience)
• Multi-platform single-cell RNA-seq expertise: hands-on analysis of data from at least two different platforms, including platform-specific troubleshooting and quality control
• Multi-modal genomics proficiency: experience with ChIP-seq, CUT&RUN, or ATAC-seq analysis including peak calling, differential accessibility, and TF motif enrichment
• Computational TF identification background: applied computational methods (GRN inference, chromatin accessibility, perturbation screens, ML) to identify transcription factors for cell fate conversion beyond literature-curated lists
• Cellular reprogramming knowledge: prior research experience (computational or experimental) in cell fate conversion, direct reprogramming, transdifferentiation, iPSCs, or differentiation systems with deep understanding of transcriptional regulation
• Strong programming skills: Python and R with proficiency in Scanpy/Seurat, standard genomics toolkits, and statistical analysis for high-dimensional data
• Foundation model interest: familiarity with transformer architectures in genomics through coursework/self-study/application, OR strong demonstrated interest with clear learning approach
• Strong publication record and demonstrated cross-functional collaboration with experimental biologists

Preferred qualifications

• Experience fine-tuning or applying genomic foundation models with demonstrable results, or contributions to bioinformatics tools incorporating transformer architectures
• Prior computational research in cellular reprogramming; extensive experience with multiple GRN inference methods and perturbation response modeling
• Background in trajectory inference beyond basic pseudotime, GRN for biological networks, or Bayesian approaches to genomics
• GPU cluster experience for model training, multi-omics integration methods, and cross-species genomics analysis

What we offer

• On-site work in the US Pacific Northwest in state-of-the-art facility
• Unique opportunity at early-stage biotech startup where you'll shape computational strategy from the beginning, build infrastructure from scratch, and have direct impact on our computational approaches for years to come
• Autonomy and ownership to design analysis frameworks, choose methodologies, and pioneer novel approaches without bureaucratic constraints, with competitive compensation including equity participation
• Mission-driven impact developing novel technologies for fertility medicine
• Competitive compensation, equity participation, and comprehensive benefits