Bioinformatics Data Engineer

Rivercell; Paris; Ibrido; EUR 70.000 - 120.000; Tempo pieno

Vantaggi offerti da questo lavoro

Equity package

Competitive salary

Descrizione del lavoro

Rivercell is seeking a Bioinformatics Data Engineer to join our team and help turn sequencing and imaging data into analysis-ready, versioned datasets. You will collaborate with ML scientists, biologists, chemists and engineers to improve data quality, harmonization and reproducibility across experiments.

You will build end-to-end pipelines for single-cell omics and imaging, implement dataset versioning and release processes, and ensure data infrastructure scales to tens of terabytes in the

Competenze

• MSc or PhD in bioinformatics, computational biology, CS, or related field.
• 3+ years building bioinformatics pipelines in production.
• Processed single-cell RNA-seq data from raw reads (Cell Ranger, STARsolo, kallisto/bustools, alevin-fry, or equivalent).
• Strong Python and software engineering: containers, tests, CI, and documentation.
• Experience with data versioning on large datasets and cloud/HPC infrastructure.
• Experience with image processing at scale, ideally microscopy (segmentation/tracking/feature extraction).
• Experience with high-performance data management (Zarr, Parquet, OME-Zarr).
• Fluent in English; French helpful but not required.

Mansioni

• Build omics pipelines from FASTQ to analysis-ready matrices: alignment, quantification, cell calling, ambient RNA handling, quality control.
• Build imaging pipelines for live-cell data: illumination correction, segmentation, tracking, feature/embedding extraction.
• Harmonize public datasets with Rivercell data.
• Set up dataset versioning, lineage, and release management for reproducibility.
• Build tooling for contamination-controlled train/test splits for benchmarks.
• Manage data infrastructure: cloud storage/compute for tens of terabytes, cost control.
• Develop data side of closed lab-in-a-loop: experiments proposed by models, retraining-ready data.

Conoscenze

Python

Bioinformatics pipelines

Single-cell RNA-seq

Data versioning

Cloud computing

Image processing at scale

English fluency

Formazione

MSc or PhD in bioinformatics/computational biology/CS

Strumenti

Cell Ranger

STARsolo

kallisto/bustools

alevin-fry

AnnData/scverse

Cellpose

StarDist

CellProfiler

Zarr/OME-Zarr

Parquet

Descrizione del lavoro

The Role

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

You have an MSc or PhD in bioinformatics, computational biology, computer science, or a related fieldYou have 3+ years of experience building and running bioinformatics pipelines in production, in industry, a core facility, or a large consortiumYou 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 ecosystemYou write strong Python and apply sound engineering practice: workflow managers, containers, tests, CI, and documentationYou have experience with data versioning on large datasets, with harmonizing heterogeneous datasets, and with cloud (AWS or GCP) or HPC infrastructureYou 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 busyAn autonomous, committed, enthusiastic, fun to work with person with a "can-do", creative, and challenging mindsetYou have excellent communication skills and are fluent in English. Professional proficiency in French is helpful but not required.