About the roleWe need a Data-Scientist who can sit at the interface of Ultrasound, synthetic-biology, and research platforms (imaging, sequencing) and drive the development of rigorous data-analysis pipelines that surface actionable insights for experimental and computational researchers alike. This person will collaborate with domain experts in ML, Ultrasound, synthetic biology, and data-engineering to develop data-analysis pipelines that transform "raw" data into actionable artifacts. This person will subsequently collaborate with ML researchers to develop and run ML pipelines that enable de-novo design and closed-loop active learning cycles across a number of problem domains in bioengineering (delivery, immunology, synthetic biology, protein-engineering).
In this role, you will:- Collaborate with wet-lab scientists to define tractable optimization objectives and metrics, and encode domain specific priors and constraints for downstream computational modeling.
- Stay current with research in Synthetic Biology and ML-guided molecular and cellular engineering, as well as data-analysis methods and techniques for biological data (OMICS, Agentic-workflows).
- Contribute to the long-term research roadmap and serve as a thought-leader for scientists.
You might thrive in this role if you have:- Deep grounding in synthetic biology, molecular engineering, and computational methods for data-analysis.
- Experience in biomolecular ultrasound.
- Familiarity with fundamental concepts in NGS, Omics, ML, and molecular engineering.
- Proficiency in Python / PyTorch / BoTorch / Pyro and comfort writing clean, reproducible production grade code.
- Experience bridging machine learning and experimental science, especially working with sparse, noisy, and or high-cost data.
If you're excited about this role but don't meet every qualification, please apply. As we build, we're hiring for complementary strengths to form a high-impact team.
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