Senior Machine Learning Scientist - AI Virtual Cells

Rivercell; Paris; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Equity package

Growth opportunities

Job summary

Rivercell seeks a Senior Machine Learning Scientist to develop and evaluate AI virtual cell models, contributing to the technical roadmap and collaborating with biology and engineering teams.

You will train models on single-cell or perturbation data, contribute to publications, and help shape Rivercell's path in techbio with equity and growth opportunities.

Qualifications

• PhD or equivalent industry research experience in ML or related field.
• Hands-on experience training models on single-cell or perturbation data.
• First-author publication in a top venue or demonstrable adoption in the field.
• Strong PyTorch fluency, including distributed training on multi-GPU or multi-node setups.
• Experience with single-cell data engineering ecosystem and real-world data messiness.
• Solid engineering practices: versioned data, pinned dependencies, reproducible analyses.

Responsibilities

• Implement, train, and test AI virtual cell models.
• Contribute to the technical roadmap and research priorities.
• Identify and harmonize useful cellular datasets with the biology team.
• Develop benchmarks and evaluation tools for industrial users.
• Maintain reproducible training code and analyses for the team and collaborators.
• Debug data pipelines and improve model performance and compute efficiency with engineering colleagues.
• Contribute to preprints/publications and conference materials.
• Present findings to AI-lab and pharma audiences and translate claims for investors.

Skills

PyTorch fluency

Distributed training

Research experience

Scientific communication

Education

PhD in ML or related field

Tools

Python

Jupyter

Job description

The role

You will join Rivercell's team as a Senior Machine Learning Scientist, contributing to our work on AI virtual cells. You will develop and evaluate models, contribute to the technical roadmap, and work with colleagues on research and implementation.

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.

• Implement, train, and test AI virtual cell models.

• Contribute to the technical roadmap and research priorities through literature reviews and experimental findings.

• Identify and harmonize useful cellular datasets with the biology team

• Develop benchmarks and evaluation tools relevant to industrial users.

• Maintain reproducible training code and analyses for the team and external collaborators.

• Debug data pipelines and improve model performance and compute efficiency with engineering colleagues.

• Contribute to preprints / publications, conference publications, and public benchmark releases.

• Present findings and help respond to questions from reviewers and collaborators.

• Support technical material for partners and fundraising.

• Opportunity to work on a cutting-edge project in techbio

• 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

Requirements

• You have a PhD in machine learning, computational biology, or an adjacent field, or equivalent industry research experience

• You have hands-on experience training models on single-cell or perturbation data

• You have at least one first-author publication in a relevant venue (Nature Methods / Cell Methods family, NeurIPS / ICLR / ICML main track or workshop, or a preprint with demonstrable adoption in the field)

• You have strong PyTorch fluency, including distributed training on multi-GPU or multi-node setups

• You are comfortable with the single-cell data engineering ecosystem and its real-world messiness

• You have strong engineering practices: versioned data, pinned dependencies, reproducible analyses, documented decisions

• You have strong scientific communication skills, written and spoken: write papers, present results to AI-lab and pharma audiences, and translate technical claims into investor-facing material

• You have a make-it-work-make-it-better mentality: comfortable shipping the first version end-to-end on a pre-seed timeline, choosing pragmatism over elegance, and returning to harden the parts that proved load-bearing