Data Scientist, Bioengineering & Brain-Computer Interfaces

Merge Labs; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Merge Labs in San Francisco, CA seeks a Data-Scientist to sit at the interface of Ultrasound, synthetic-biology, and research platforms, developing rigorous data-analysis pipelines that surface actionable insights for experimental and computational researchers.

You will collaborate with ML researchers and domain experts to transform raw data into actionable artifacts and to enable de-novo design and closed-loop active learning across bioengineering domains.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

Data analysis

ML pipelines

Experimental science bridging

Tools

PyTorch

BoTorch

Pyro

Job description

Merge Labs in San Francisco, CA seeks a Data-Scientist to sit at the interface of Ultrasound, synthetic-biology, and research platforms, developing rigorous data-analysis pipelines that surface actionable insights for experimental and computational researchers.

You will collaborate with ML researchers and domain experts to transform raw data into actionable artifacts and to enable de-novo design and closed-loop active learning across bioengineering domains.