Bioinformatics Data Engineer

Join Rivercell as a Bioinformatics Data Engineer, where you will contribute to the development of AI virtual cell models. You will be responsible for building and maintaining pipelines that turn sequencing and imaging data into analysis-ready datasets. This role offers the opportunity to work on a cutting-edge project in the biotech field, with competitive salary and equity package, and exciting opportunities for personal and professional growth.

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

Responsibilities

• Contribuer à la création et à l'évaluation de modèles de cellules virtuelles en développant et en maintenant des pipelines de données.
• Construire des pipelines omiques, en commençant par les cellules uniques, de FASTQ à des matrices prêtes à l'analyse.
• Gérer l'infrastructure de données, y compris le stockage et le calcul dans le cloud, pour des volumes de données importants.
• Multimodal single-cell data and cross-modality cell matching
• Sample tracking or LIMS integration, and daily work alongside a wet lab

Requirements

• You have 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.
• Open-source bioinformatics contributions (nf-core, scverse) or public dataset releases (GEO, CELLxGENE, Hugging Face)