The Impact You'll MakeAs a Machine Learning Intern, you will contribute to building ML models leveraging a range of data types including vision and other Omics that will power a diverse range of use cases in drug discovery. By leveraging your experience in machine learning, software engineering, infrastructure, and data, you will:Contribute to developing algorithms and architectures and training models on large biological datasets Leverage Recursion's leading supercomputer, training models across 100s of GPUsExplore properties of the resulting representation model on a range of benchmarksWork collaboratively alongside machine learning engineers, scientists, and product managersLocation:Toronto, Ontario CAThe Team You'll Join:You will be joining Inception Labs, a research and development team within Recursion. This team is a cross-functional group of exceptional biologists, engineers, product managers, machine learning scientists, computational biologists, and data scientists. Together, we are working to prove out novel technologies including new biological assays and data modalities, statistical and ML techniques, and computational approaches for multimodal data. This team works rapidly on a project-by-project basis to either prove or disprove the value and feasibility of these new ideas and approaches. Recursion has built a unique dataset (over 1 million unique biological perturbations profiled in a high-dimensional biological assay, making up nearly 20 PB of highly-relatable data) and a unique approach to phenotypic drug discovery using our inference-based Recursion Map to predict tens of billions of relationships between disease models and therapeutic candidates. Additionally, we've generated additional large-scale Omics datasets to complement our phenomics data. Recently, Recursion has also gained access to large numbers of anonymized patient records including medical imaging and molecular profiling artifacts to guide our understanding of disease mechanisms. The capabilities you bring to the Inception Labs team will help us scale our machine learning models using these and other data sets to develop net-new capabilities that don't currently exist!The Experience You'll NeedExpert knowledge of deep learning theory and practice including the ability to:Experience working with biological data (microscopy, genetics, rnaSeq, etc.)Contribute to the development or implementation of deep learning algorithms and architectures, particularly leveraging self-supervised learningRun large scale experiments evaluating 100s of hyper parameters Run and interpret benchmarks for assessing model performanceContribute to conference and journal publicationsTools: PyTorch, NumPy, Pandas, GCP, Hybrid Cloud, Linux, CUDA, Docker, Kubernetes, BigQuery, large scale distributed systems
