Noetik is an AI-native biotechnology company that applies self-supervised machine learning to large-scale human multimodal spatial omics datasets with the aim of developing precision cancer therapies. Backed by DCVC (Data Collective).
About the role
You'll join a cross-functional therapeutics team responsible for surfacing promising therapeutic opportunities, pressure-testing their scientific and translational logic, and shaping how programs move through the pipeline. We're intentionally building a different kind of therapeutics group to support this work, because turning human foundation models / insights into new therapeutic concepts and real patient impact requires a fundamentally different way of working.
What they're looking for
- PhD, MD, or equivalent experience in cancer biology, immunology, pharmacology, molecular biology, translational medicine, or a related discipline
- Bona fide experience on drug discovery or development programs, with a working feel for the scientific, operational, and strategic go/no-go decisions required to move a program forward… or kill it
- Strong intuition for turning hypotheses into viable therapeutic programs, spanning target validation and modality considerations through translational strategy and positioning
- Genuine comfort in a room that mixes biology, machine learning, computational science, and strategy, where no one person holds the singular answer
- Hands-on use of modern AI tools in scientific work, with examples of how they’ve changed the way you investigate questions, evaluate evidence, or make therapeutic decisions
- A systems-thinking mindset with the ability to prioritize across competing scientific opportunities
More about this role
NOETIK is a frontier therapeutics company harnessing AI to deeply understand human biology and redefine clinical outcomes in oncology. Leveraging the largest proprietary multimodal oncology dataset of its kind, uniquely integrating deep spatial profiling with routine clinical assays, NOETIK’s foundation AI models precisely match therapeutic targets to patient subpopulations and accurately predict clinical efficacy. This AI-first approach elevates therapeutic development from probabilistic guesswork to precision science.
Drug discovery and development is a decision-making problem. We are generating more data and building better models, but real impact comes from knowing which questions matter and what evidence actually impacts a program’s trajectory. We need scientists who can pair model-driven outputs with sharp therapeutic judgment to figure out which therapies should exist, and for whom .
Ultimately the gig is translating those outputs into defensible portfolio calls. This means being deep in biology and therapeutic concepts, while also being genuinely engaged with frontier AI and how that is changing how we work. You will be the architect of new and better systems for...
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