Bioinformatics Data Engineer

You will join Rivercell's team as a Bioinformatics Data Engineer , contributing to the datasets used to train and evaluate our AI virtual cell models. You will develop and maintain pipelines that turn sequencing and imaging data into versioned, analysis-ready datasets. Working with our ML scientists, biologists, chemists, and engineers, you will help improve data quality, harmonization, and reproducibility. The Challenge We believe the AI virtual cell, a world model that can predict how cells will respond before an experiment runs, is the biology challenge of our times.

Rivercell's cell-on-a-chip technology offers the opportunity to develop models alongside the platform generating their training data. You will work with our biology and engineering teams to explore which experiments, data, and architectures make those predictions more reliable. Our goal is to make biological research faster and more predictive, with models that help researchers decide what to test next.

Build our omics pipelines, starting with single-cell, from FASTQ to analysis-ready matrices: alignment and quantification, CRISPR guide assignment, cell calling, ambient RNA and doublet handling, and per-run quality control

Build the imaging pipelines for live-cell brightfield and fluorescence time-lapse data: illumination correction, segmentation, tracking, and feature and embedding extraction Internalize and harmonize relevant publicly available datasets with Rivercell's own data Set up dataset versioning, lineage, and release management, so that every dataset behind a model, a paper, or a benchmark can be traced to its raw data and pipeline version and every experiment is reproducible

Build the tooling that generates and audits contamination-controlled train/test splits for our benchmarks Run the data infrastructure: cloud storage and compute for tens of terabytes, cost control, and data loading fast enough to keep GPUs busy

Build the data side of our closed lab-in-a-loop, in which models propose experiments, the lab runs them, and the results return as retraining-ready data Opportunity to work on a cutting-edge project in the field of biotech Opportunity to join an ambitious team and impactful startup among the first employees Competitive salary and equity package Exciting opportunities for personal and professional growth within the team

an MSc or PhD in bioinformatics, computational biology, computer science, or a related field You have 3+ years of experience building and running bioinformatics pipelines in production, in industry, a core facility, or a large consortium You have processed single-cell RNA-seq data from raw reads (Cell Ranger, STARsolo, kallisto/bustools, alevin-fry, or equivalent) and work comfortably in the AnnData / scverse ecosystem You write strong Python and apply sound engineering practice: workflow managers, containers, tests, CI, and documentation You have experience with data versioning on large datasets, with harmonizing heterogeneous datasets, and with cloud (AWS or GCP) or HPC infrastructure

You have experience with image processing at scale, ideally microscopy: segmentation, tracking, and feature or embedding extraction (Cellpose, StarDist, CellProfiler, or deep-learning models) You have experience with high-performance data management for model training: array and columnar formats (Zarr, TileDB-SOMA, Parquet, OME-Zarr), sharding and streaming from object storage, and PyTorch data loaders that keep GPUs busy An autonomous, committed, enthusiastic, fun to work with person with a "can-do", creative, and challenging mindset You have excellent communication skills and are fluent in English.

Professional proficiency in French is helpful but not required. Please let us know in your application if you specifically have experience with one or several of the following items: Pooled CRISPR screen or Perturb-seq data, including guide calling Multimodal single-cell data and cross-modality cell matching Sample tracking or LIMS integration, and daily work alongside a wet lab Open-source bioinformatics contributions (nf-core, scverse) or public dataset releases (GEO, CELLxGENE, Hugging Face)

Throughout the recruiting process, you will meet 4 to 6 team members of Rivercell. Introductory call (up to 30mn, videocall) Technical interview (up to 90mn) Performance interview (up to 90mn, in person) Culture interview (up to 60mn, in person)

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