Scientist - Synthetic Biology

Merge Labs • San Francisco, CA

18h

About The Position

Merge Labs is a frontier research lab focused on bridging biological and artificial intelligence to maximize human ability, agency, and experience. Its Bio team develops biotechnologies for next-generation brain-computer interfaces, combining molecular engineering, synthetic biology, neuroscience, and physical methods such as ultrasound. The team develops and validates molecular technologies in vitro and in vivo, demonstrates capabilities in animal models, and collaborates across disciplines with engineers and data scientists.

The Scientist in Synthetic Biology will design, build, and optimize genetic payloads that control how, how much, where, and when molecular tools are expressed in the brain. The role includes developing mammalian gene circuits and multigene architectures to control stoichiometry, timing, and cell-type specificity, as well as establishing scalable workflows for rapid testing. In collaboration with protein engineers, cell biologists, the delivery team, and in vivo scientists, the scientist will advance promising designs from neuronal models to in vivo validation. This work will establish expression-control foundations for future brain-computer interfaces.

Requirements

• PhD in Synthetic Biology, Bioengineering, Molecular Biology, or a related field, or equivalent experience leading research projects end-to-end.
• 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.
• Mechanistic approach and motivation to understand how things work, not just whether they work.
• Independence to define a research strategy, troubleshoot at the bench, and drive ambiguous programs from concept through interpretable data.
• Ability to collaborate and communicate naturally with experts across disciplines.
• Enthusiasm for 0-to-1 innovations and working on frontier scientific and technical problems in a fast-paced team.

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, including 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 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 the in vivo operations team.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree