Machine Learning Engineer

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.