Hard Technical Challenges
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The company sits at the edge of machine learning, biology, and experimental science. Some of the hardest problems you’ll work on include:
Learning from sparse biology.
Biological data is noisy, expensive, high-dimensional, and incomplete. How do we learn useful representations of cellular state from limited experimental data?
Building models scientists can trust.
Cells contain real biological structure: metabolism, regulation, signalling, transport, and stress responses. How do we combine this knowledge with ML to build models that are predictive and biologically meaningful?
Guiding better experiments.
Useful models should help scientists understand uncertainty, compare hypotheses, and decide what to test next. How do we evaluate models when there is no clean benchmark for “understandin...