Data Scientist, Bioengineering

Slope; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Merge Labs in San Francisco seeks a Data-Scientist to bridge ultrasound, synthetic biology and research platforms, building rigorous data-analysis pipelines that surface actionable insights for experimental and computational researchers.

You will collaborate with ML researchers to develop and run ML pipelines that enable de-novo design and closed-loop active learning across bioengineering domains, translating raw data into reproducible, production-grade code.

Qualifications

• Experience bridging ML and experimental science.
• Experience with sparse, noisy, or high-cost data.
• Proficiency in Python and ML frameworks for data-analysis pipelines.

Responsibilities

• Collaborate with wet-lab scientists to define tractable objectives and encode domain priors for modeling.
• Stay current with synthetic biology, ML-guided molecular engineering, and data-analysis methods for biological data.
• Contribute to long-term research roadmap and act as a thought-leader for scientists.

Skills

Python

Data analysis

Machine learning

Tools

PyTorch

BoTorch

Pyro

Job description

Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. We’re pursuing this goal by developingfundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone to use.

About the team

Merge is building the next generation of brain-computer interfaces by combining recent advances in synthetic biology, neuroscience, AI, and non-invasive imaging. To support this mission, we are building a cross-functional data-science group which sits at the intersection of computational modeling, neuroscience, and biomolecular engineering. This group collaborates extensively with wet-lab scientists, automation engineers, and data engineers to create ML frameworks that accelerate molecule discovery and device optimization.

About the role

We 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.

Merge Labs does not discriminate on the basis of race, color, religion, national origin, age, sex, sexual orientation, gender, gender identity, gender expression, marital status, physical or mental disability, medical condition, genetic information, family status, ancestry, citizenship, U.S. military (state and federal) and veteran status, or any other legally protected status. It is our intention that all applicants be given equal opportunity and that selection decisions are based on job related factors. We are an equal opportunity employer.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing [e-mail].