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Deep Learning Research Scientist - Foundation Model on Omics data

DeepLife is hiring!

About

DeepLife is a series A startup focused on addressing the urgent need to increase drug discovery reliability by acting on the earliest step, drug target identification. This consists of identifying, a molecular target, such as a protein, that will trigger the transition from disease to healthy cells. With current methods, only 1 target in 10,000 reach the market, leading to a significant loss of time and efforts in the community.

Our approach is to leverage the recent revolution in the omics data, measuring precisely cells activity at large scale, and build foundation models to mimic cell behavior in various contexts and identify the optimal trigger to reverse disease state.

Half of the team today is dedicated to build the largest omics database, aka omics atlas, to map all human body tissues and diseases and reduce experimental biases.

We offer a research friendly environment, with 90% of the company holding a PhD, with academic collaborations and publications. The team is international and composed of +10 different nationalities. The company is remote first with most of the work is remote and regular events organized in our offices in Paris.

Job Description

Overview:

In this role, you will develop a novel cell embedding that integrates multiple omics foundation models—such as transcriptomics, proteomics, epigenomics, and metabolomics—to capture comprehensive, multi-dimensional cellular signatures. Your innovations will be pivotal in predicting drug effects on cell types and tissues, transforming raw data into actionable insights in drug discovery.

Key Responsibilities:

• Deep Learning Model Development: Design and implement large-scale deep learning models that integrate diverse omics datasets to build robust cell embeddings. These embeddings will serve as the foundation for our digital twin technology, enabling precise predictions of drug effects at both cellular and tissue levels.

• Multi-Omics Integration: Develop and refine foundation models across various omics platforms, combining them into a unified cell embedding that reflects the complex molecular landscape of cells.

• Digital Twin development: Design and implement large-scale causal model to predict cell response to perturbations.

• Cross-Disciplinary Collaboration: Partner with experts in omics, bioinformatics, and drug discovery to ensure seamless integration of multi-modal data and validate model predictions through collaborative research efforts.

• Client & Partner Engagement: Work closely with the product and service teams on projects with clients and strategic partners, translating advanced AI models into impactful drug discovery solutions.

• Research Leadership: Continuously monitor emerging trends in AI and omics technologies, contributing to scientific publications and driving innovation within our international, interdisciplinary team.

What We’re Looking For:

• A visionary researcher passionate about leveraging deep learning to solve complex biological challenges in drug discovery.

• Proven expertise in integrating multi-omics data to develop innovative computational models that generate actionable insights.

Preferred Experience

Qualifications (Ranked by Importance):

• PhD or Postdoctoral Experience in Computer Science: Demonstrated expertise, evidenced by publications in top-tier machine learning conferences (e.g., NIPS, ICLR, ICML, UAI, CVPR).

• Strong Foundation in Machine Learning/Applied Mathematics: Robust academic background in advanced ML techniques and applied mathematics.

• Experience with Large-Scale Deep Learning Models: Proven track record in building and scaling AI models, particularly those applied to omics or other high-dimensional biological data.

 Experience with Probabilistic Graphical Models and Causal Inference: Proven track record in designing, building, and scaling advanced causal inference frameworks, including Bayesian networks, structural equation models, and counterfactual analysis methods.

• Multi-Omics Integration Expertise: Experience in developing and combining foundation models across different omics datasets to create unified cell embeddings.

• Collaborative Mindset: Prior success working within interdisciplinary teams and managing cross-functional projects.

Why Join DeepLife?

At DeepLife, you’ll be at the forefront of a transformative era in drug discovery. By pioneering multi-omics cell embedding techniques, you’ll help shape the future of personalized medicine and digital twin technology in biology. Enjoy the creative freedom to innovate, work alongside international experts, and contribute to projects that have a direct impact on human health.

Ready to transform cellular data into life-saving insights? Apply today and be a key driver of innovation at DeepLife!

Additional Information

  • Contract Type: Full-Time
  • Location: Paris
  • Education Level: PhD and more
  • Possible full remote
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