Bioinformaticien Senior

Location

Paris 14e, Cochin. Full remote possible from anywhere in France.

Start date

Flexible

Contract

Full-time, permanent (CDI)

About Generare

We are reopening one of humanity's most powerful sources of innovation: the chemistry that microbes have been evolving for billions of years. Roughly 97% of it has never been read. Our discovery engine reads that chemistry at industrial scale and turns it into novel bioactive molecules to build the medicines and crop protection of the future.

Founded in 2023 as an INSERM spin-out by Guillaume Vandenesch and Dr Vincent Libis, Generare raised a €20M Series A in April 2026, led by Alven and Daphni. We are 32 people in the heart of Paris, and we will be close to 40 by the end of the year.

Role overview

Our discovery engine starts with DNA. Every week, our lab sequences thousands of microbial genomes and amplicons, and the questions that follow decide which experiments we run next. We are looking for a Senior Bioinformatician to join our R&D team and turn those questions into answers the lab can act on.

You will own projects end to end. You will sit down with wet-lab colleagues to scope the question, design the analysis or the tool, deliver it, and hand it over so others can use it. Some of your work will become software bricks running inside our production workflow. Some of it will be analyses that directly shape the next round of experiments. Either way, you will see the impact of your work at the bench, quickly.

This is an individual contributor role in a small, distributed bioinformatics team. We are looking for someone who enjoys autonomy, builds things that last, and cares as much about the biological question as about the code.

Key responsibilities

1. Project ownership

• Scope biological questions with wet-lab colleagues and turn them into well-defined analyses or tools.

• Deliver results that guide the next wet-lab experiments, and explain them clearly to non-bioinformaticians.

• Hand over what you build so others can use it, maintain it and trust it.

1. Software and pipelines

• Build software bricks that integrate into our production workflow.

• Develop and run pipelines with Nextflow and cloud batch.

• Apply sound engineering practices: version control, testing, packaging, containers, documentation.

1. Data analysis

• Analyse Nanopore sequencing data (amplicon and whole-genome) from raw reads to final results.

• Bring statistical rigour and good experimental design to every analysis.

• Use AI coding assistants to move faster, while reviewing, testing and owning the code they produce.

1. Ways of working

• Run several projects in parallel, favouring actionable answers over exhaustive ones.

• Set up much of your own tooling, with light IT and dev support.

Profile

What we are looking for

• Experience: 3+ years after your degree or PhD as a main contributor on engineering or scientific projects, with at least one deliverable used by others.

• From question to answer: You can turn a biological question into a scoped analysis and explain the results clearly to non-bioinformaticians, in English.

• Python: You write professional Python, including data analysis (pandas, NumPy, plotting), with sound statistics and experimental design.

• Engineering practices: You use git, testing, packaging, containers and documentation as a matter of course.

• AI-assisted coding: You use AI coding assistants fluently, and you have the judgment to review, test and own what they generate.

• Workflows at scale: You have built pipelines with Nextflow or Snakemake and run them on HPC (SLURM) or cloud batch.

• Nanopore: You have analysed Nanopore sequencing data (amplicon and/or WGS).

• Language: You work comfortably in English.

Bonus points

• Deep expertise in biosynthetic gene cluster (BGC) mining: antiSMASH, BiG-SCAPE, BiG-SLiCE. This is a strong plus.

• Nextflow specifically.

• Metagenomics.

• HRMS or metabolomics data.

• Building relational databases.

• Curating large sequence databases.

• Hands-on wet-lab experience, or prior work embedded in a wet-lab team.

• Machine learning (scikit-learn or similar).

How we work

• English is our working language for meetings, documentation and code.

• The bioinformatics team is small and dist