Bioinformatics Scientist

What you´ll do

Lead end-to-end TF discovery for cellular reprogramming - from multi-platform single-cell genomics analysis (scRNA-seq, ATAC-seq) through GRN inference, differential analysis, and trajectory mapping - to nominate the regulators that flip cell fate.

Crack the combinatorial code of reprogramming by ranking TF cocktails as actionable combinations and decoding pooled perturbation and CRISPRa screens at single-cell resolution.

Read regulatory grammar straight off the chromatin - accessibility, motifs, synergy, repression - and build the data backbone that harmonizes modalities and platforms into something we can actually model on.

Sit shoulder-to-shoulder with wet lab teammates, closing the loop between predictions and screens: ingest fresh NGS readouts, retrain, re-prioritize, and pick the next experiment that teaches the model the most.

Core requirements

PhD in Bioinformatics, Computational Biology, or related quantitative field (or MS with 5+ years relevant industry experience);

Demonstrated track record applying computational TF ranking and GRN inference to cellular reprogramming problems, transdifferentiation, directed differentiation, or iPSC systems;

Multi-platform single-cell RNA-seq expertise: hands-on analysis from at least two different platforms, including platform-specific troubleshooting and quality control;

Multi-modal genomics proficiency: ChIP-seq, CUT&RUN, or ATAC-seq analysis including peak calling, differential accessibility, and TF motif enrichment;

Hands-on experience with established GRN inference methods to nominate or rank regulators of cell state, beyond literature-curated lists;

Experience analyzing pooled perturbation screens (CRISPRa, CRISPR knockout, or barcoded TF overexpression) with single-cell or bulk readouts;

Working knowledge of trajectory inference and pseudotime methods for mapping cell state transitions;

Strong programming skills in Python and R, with proficiency in Scanpy/Seurat and statistical analysis for high-dimensional data;

Comfortable working in a modern computational environment: cloud platforms, workflow managers, containerization, and collaborative version control;

Strong publication record and demonstrated cross-functional collaboration with experimental biologists.

You´ll stand out with

Direct experience nominating or validating TF cocktails that successfully induced a cell state conversion (published or in preparation).

Experience with dynamical systems modeling for cell state transitions, or inverse problem approaches for TF combination ranking.

Background in advanced trajectory inference (optimal transport, GRN dynamics over pseudotime), Bayesian genomics, multi-omics integration, or cross-species comparative regulatory genomics.

Familiarity with transformer architectures in genomics and interest in hybrid classical/ML approaches to gene regulation.

Why e184?

Unrivaled impact: Your work directly enables technology that transforms human fertility and reproductive medicine.

Full-spectrum growth: Gain exposure to the entire lifecycle of discovery. From screening to mechanistic validation.

Best of both worlds: Experience the creative chaos of an early-stage startup with the stability of a well-capitalized company.

Elite collaboration: Work alongside a world-class team who are as driven as you are.

What we offer

Competitive salary + equity participation is considered

State-of-the-art facility in Portland metro area

Comprehensive Medical, Dental, Vision, and 401(k) with company match

20 days PTO + 11 paid holidays