MACHINE LEARNING ENGINEER

About

Scienta Lab is a deeptech company harnessing artificial intelligence to transform the drug discovery and development process in immunology and inflammation.

With its unique and proprietary EVA foundation model dedicated to immune-mediated diseases, Scienta Lab leverages multimodal data to bridge the gap of translational research, accelerate the validation of new therapeutic targets and the development of personalized treatments. The company’s research activities, led in partnership with top-tier academic institutions across Europe, are regularly featured in medical journals and international congresses.

Scienta Lab is based in Biolabs Hôtel Dieu and has been selected amongst the 2023 edition of the Future 40 program which rewards the most promising startups of Station F each year. In December 2023, the company announced a seed round of € 4M from CentraleSupélec Venture and a team of world-class business angels and is thus seeking for top talents to accelerate its development. In June 2025, Scienta Lab was laureate of the EIC Accelerator program, selected as one of the most innovative deeptech startups across Europe, providing a significant funding to the company.

We are a team with diverse backgrounds in AI, Computational Biology, Immunology and pharma industry, with half the company with PhDs. Join us and be a part of our exciting journey to unravel the mysteries of the immune system!

Job Description

Discover who we are and what we value here.

As a Machine Learning Engineer at Scienta Lab, you will be at the forefront of developing and scaling our cutting-edge EVA foundation model for immunology and inflammation diseases. You will design, implement, and optimize ML pipelines to train and apply our models to biomedical data, including bulk and single-cell RNAseq, histology, and large-scale clinical data, working closely with computational biologists, immunologists, and AI researchers in a collaborative, multi-disciplinary environment.

Your role will be crucial in advancing our mission to revolutionize drug development by building robust, scalable ML infrastructure that supports both research and production applications for pharmaceutical and biotech partners.

We value high-level engineering practices and aim to develop a culture of continuous improvement in the team. We implement best practices to deliver production-ready ML systems and contribute to top-tier scientific publications.

Main Missions

• Model Development & Optimization: Design, implement, and optimize transformer-based foundation models for transcriptomic, histology or clinical data, with focus on pre-training, fine-tuning, or benchmarking those models at scale.
• ML Infrastructure & MLOps: Build and maintain scalable ML pipelines, including data preprocessing, model training, evaluation, and deployment infrastructure. Implement MLOps best practices for model versioning, monitoring, and automated retraining
• Data loading and scaling: Transform our raw datasets into deep-learning-ready data loaders, that can scale to millions of datapoints.
  
  

Preferred Experience

• Master's degree or PhD in Machine Learning, Computer Science, Data Science, or related field
• 3+ years of hands-on experience with deep learning frameworks (PyTorch, TensorFlow, Jax)
• Strong Python proficiency with experience in production ML systems, including containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure)
• MLOps & DevOps: Experience with ML pipeline frameworks (Kubeflow, MLflow, Weights & Biases, Metaflow), version control (Git), CI/CD practices, and infrastructure as code
• Experience with GPU programming, distributed training, and model optimization techniques
  
  

How To Stand Out

• You have high agency, look for solutions rather than problems and you are a team-player willing to go beyond your job description
• Experience with biological or healthcare data, particularly transcriptomics or histology data
• Experience with foundation models in NLP or other domains
• Experience with multimodal data integration
• You are experienced in working in dynamic and changing environments such as startups
  
  

Additional Information

• Contract Type: Full-Time
• Location: Paris
• Possible partial remote