Research Scientist (AI)\_Interactome
Palo Alto, Paris, Abu Dhabi Engineering / Full Time / On-site
Job Requirements
+ PhD (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
+ Proven track record in research and innovation demonstrated through contributions in top-tier AI/ML (e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICLR) and/or core biology (e.g., Nature, Science, or Cell) journals and conferences.
+ Skilled in developing, implementing, and debugging deep learning methods/models in popular frameworks, such as JAX, TensorFlow, or PyTorch, with an interest in generative models, graph neural networks, or large-scale deep learning applications.
+ A strong theoretical foundation (statistics, optimization, graph algorithms, linear algebra) with experience building models ground up.
+ A passion for interdisciplinary research (with an emphasis on the intersection of AI and Biology), and willingness to acquire necessary domain knowledge.
+ Motivated and self-driven with the ability to operate with partial and incomplete descriptions of high-level objectives (as is typical in a start-up environment).
+ Evidence of familiarity and utilization of software engineering best practices (version controlling, documentation, etc), and open-source contributions, especially if used by others.
Qualifications
+ 3+ years of post-PhD experience in an industry or postdoc role
+ Prior experience working at either a start-up or top research industry labs (e.g., OpenAI, FAIR, Deepmind, Google Research).
+ Hands-on prior experience working at the intersection of AI and Biology.
+ Experience in large-scale distributed training and inference, ML on accelerators.
Preferred Qualifications
+ Prior experience working with diverse biological datasets, including but not limited to bulk/single transcriptomics (e.g., RNA-Seq), epigenetic (e.g., ATAC/ChIP-Seq), proteomics/phosphoproteomics (e.g., mass-spec), and genetics (e.g., GWAS) datasets.
+ Familiarity with diverse biological networks, including but not limited to protein-protein interaction, gene-gene expression, and TF-Target Gene regulatory networks.
+ Prior experience developing algorithms for network/systems biology (e.g., network construction/inference, clustering, embedding, etc)
+ Familiarity with Graph ML frameworks, such as Pytorch Geometric, Deep Graph Library (DGL), and Nvidia RAPIDS (cuGraph/cuML).
+ Hands-on experience with geometric deep learning models such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).
+ Familiarity with traditional (e.g., TransE, RotatE, etc.) and deep (ULTRA) representation learning algorithms for large knowledge graphs
