Research Scientist (AI) - Interactome

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