Scientist - Synthetic Biology

Merge Labs•San Francisco, CA

2d•$120,000 - $165,000

About The Position

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 developing fundamentally 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. Our Bio team designs, builds, and characterizes the biotechnologies that form the foundation of next-generation brain-computer interfaces. We combine molecular engineering, synthetic biology, neuroscience and advanced physical methods such as ultrasound to establish less invasive, high-bandwidth connections with neurons. The Bio team develops our core molecular technologies, validates their performance in vitro and in vivo, and demonstrates their advanced capabilities in animal models. We build custom experimental setups and pipelines and collaborate closely with engineers and data scientists. We work across disciplines to come up with creative ideas and solve some of the most challenging problems in biotechnology. As a Scientist in Synthetic Biology, you will design, build, and optimize the genetic payloads that determine how, how much, where, and when our molecular tools are expressed in the brain. You’ll develop mammalian gene circuits and multigene architectures that control stoichiometry, timing, cell-type specificity, and establish scalable workflows to test them rapidly. Working closely with protein engineers, cell biologists, the delivery team, and in vivo scientists, you’ll advance the strongest designs from neuronal models to in vivo validation. Your work will set the expression-control foundation for next-generation brain-computer interfaces.

Requirements

A PhD in Synthetic Biology, Bioengineering, Molecular Biology, or a related field, or have equivalent experience leading research projects end-to-end.
A strong record of designing and experimentally validating mammalian gene circuits or tunable expression systems.
Proficiency in molecular cloning, library construction, mammalian cell expression, and quantitative characterization using methods such as flow cytometry, qPCR, sequencing, imaging, or functional assays.
Experience establishing systematic or high-throughput construct-testing workflows.
Think mechanistically: you are motivated by understanding how things work, not just that they work.
The independence to define a research strategy, troubleshoot at the bench, and drive ambiguous programs from concept through interpretable data.
Feel joy in collaboration and communicate naturally with experts across disciplines.
Are energized by 0->1 innovations and want to be part of a fast-paced team working on frontier scientific and technical problems.

Nice To Haves

Experience with neuronal, iPSC-derived, or primary-cell models and in vivo translation.
Experience with payload design for viral or non-viral delivery technologies.
Expertise in RNA regulation, aptazymes, microRNA-based control, UTR and mRNA-stability engineering, intracellular immune sensing or transgene silencing, or cell-type-specific expression.
Experience engineering multicomponent protein systems or modeling circuit behavior and expression dynamics.

Responsibilities

Design and implement mammalian genetic-circuit strategies to control component stoichiometry, expression level, timing, cell-type specificity, and cellular burden in multicomponent payloads.
Build and characterize synthetic constructs at high throughput using modular libraries of promoters, UTRs, introns, polyadenylation signals, codon usage, gene order, polycistronic elements, and other regulatory sequences.
Develop compact expression-control systems such as inducible switches, dual-vector logic, feedback circuits, and microRNA-based regulation to safeguard against overexpression and off-target expression.
Engineer host-cell compatibility by addressing protein trafficking and turnover, proteostasis, metabolic burden, cellular stress, intracellular immune sensing, and transgene silencing; evaluate chaperone co-expression or degradation-control strategies where appropriate.
Translate expression requirements into payload architectures that are compatible with viral and non-viral delivery technologies.
Characterize expression and function in mammalian cells and neurons using quantitative readouts for dynamic range, leakiness, kinetics, stoichiometry, protein stability, cellular burden, stress responses, transgene silencing, and potential toxicity.
Evaluate lead payloads in animal models in collaboration with our in vivo operations team.