Research Scientist (AI) - Sequence

Job Requirements

• PhD (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, 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 PyTorch, JAX, or Tensorflow with an interest in generative models, graph neural networks, or large-scale deep learning applications
• A strong theoretical foundation (probabilistic models, 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

• Experience with genomics, transcriptomics, or proteomics data, particularly functional assays (e.g. ATAC, CAGE, Hi-C, …)
• Experience with complex data types, including multi-omics and health data (EHRs).
• Familiarity with public data repositories (NCBI, ENSEMBL, ENCODE, TCGA, UK Biobank) and experience curating datasets to answer specific scientific questions.
• Experience with methods development for afore-mentioned data types
• Experience with multimodal or multiscale models (even in other domains, e.g. remote sensing, medical imaging).
• Deep knowledge of one or more of the following: transformers, convolutional networks, discrete diffusion models, self-supervised learning, and co-embedding approaches.