e184
Bioinformatics Scientist - Gene Regulation & Transformer Modeling
e184, Portland, Oregon, United States, 97204
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
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.
Location: Portland, Oregon Metropolitan Area
Salary: $75,000 – $85,000 (4 weeks ago)
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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
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.
Location: Portland, Oregon Metropolitan Area
Salary: $75,000 – $85,000 (4 weeks ago)
#J-18808-Ljbffr