Senior ML Research Engineer (Virtual Cell)

Join our AI Sim R&D team as a Senior ML Research Engineer, where you will work on our virtual cell platform, AQCell. You will build and maintain machine learning models, manage large-scale biological datasets, and contribute to the evaluation and translation of research ideas into practical applications. This role offers the opportunity to shape the future of drug discovery through the intersection of machine learning and biology.

• Contribuer au développement, à l'entraînement et à la maintenance de modèles d'apprentissage automatique pour la prédiction de la réponse transcriptomique et des résultats fonctionnels.
• Gérer l'acquisition, l'harmonisation et la gestion de grandes bases de données biologiques, y compris l'harmonisation des schémas, la normalisation et la dé-duplication.
• Collaborer avec des biologistes computationnels, des ingénieurs logiciels et des parties prenantes pour garantir que les modèles sont fondés sur une biologie solide et utilisables dans des flux de travail de découverte de médicaments réels.

• Large-Scale Data Management: Required experience managing large-scale datasets and managing the training of models over them, including data ingestion, cleaning, versioning, and pipeline maintenance at scale
• Scientific Rigor: Ability to design sound evaluation methodology (e.g. train/test splitting strategies, held-out generalization tests) and to critically interpret model performance against meaningful baselines
• Software Engineering: Strong Python programming skills and experience with modern ML frameworks (e.g. PyTorch, JAX) and experiment tracking/data versioning tools (e.g. Weights & Biases)
• Academic Foundation: Bachelor's degree in a scientific or quantitative field (Computer Science, Physics, Mathematics, Biology, Chemistry, or related); an advanced degree (MS or PhD) is preferred
• Applied ML in Science: Demonstrated experience building and maintaining machine learning models in a scientific discipline in an industry setting, including taking models from prototype through validation and maintenance; experience in a life sciences setting is preferred
• Domain Datasets: Familiarity with public perturbation or drug-sensitivity datasets such as LINCS L1000, GDSC, DepMap, or single-cell perturbation atlases
• Bioinformatics & Computational Biology: Experience with bioinformatics and computational biology data analysis tools, particularly transcriptomics harmonization and normalization tools (e.g. batch correction, pseudobulking, gene ID mapping/standardization)
• Knowledge Graphs & GNNs: Experience with knowledge graph embeddings or graph neural networks applied to drugs, targets, or cells
• Cheminformatics: Familiarity with cheminformatics representations (SMILES, InChI keys, PubChem/Cellosaurus identifiers) used to harmonize drug and cell metadata across datasets
• Collaborative Innovation: Experience working in interdisciplinary environments where AI intersects with the biological sciences