Machine Learning Engineer, AI for Genomics

Develop robust, scalable ML software for predictive and generative modeling tasks related to genomics data (eg. Interactome, Cell & Tissue modeling). Design and implement ML algorithms to enhance NGS sequencing pipelines. Apply reasoning techniques—including LLMs, Graph Neural Networks, Gen AI models—for extracting insights to advance drug discovery from simulation, omics data, and literature. Identify, ingest, and curate relevant data sources. Own data quality control, validation, and integration workflows. Research and prototype novel bioinformatics and deep learning approaches to interpret human genetic variants, gene regulation mechanisms and disease pathways using diverse multimodal data (e.g. multi-omics, single-cell data, proteomics, genomics, biomedical imaging). Communicate complex ideas effectively across audiences, including internal collaborators, external stakeholders, and clients—tailoring technical depth as needed. Contribute to the scientific community through patent filings, peer-reviewed publications, white papers, and conference presentations.