Protein Engineer - Biosensors

Merge Labs•San Francisco, CA

7d•$120,000 - $215,000

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 Protein Engineer - Biosensors, you will design, build, and optimize the proteins that translate between the languages of cells and machines — turning neuronal activity at the molecular scale into interpretable imaging signals. You’ll work with unconventional proteins, including multi-component assemblies, and use your biophysical insights, high-throughput experimental methods, and close collaboration with our computational team to push the boundaries of what these molecules can do. Your work will set the molecular foundation for next-generation brain-computer interfaces.

Requirements

Have a PhD in Bioengineering, Biophysics, Biochemistry, or a related field, or have equivalent experience leading research projects end-to-end.
Have worked on biosensors or other proteins with novel functionality.
Are proficient in library construction, protein expression in mammalian cells, and standard characterization.
Are comfortable using computational approaches for protein engineering and understand the fundamental principles involved.
Think mechanistically and have strong biophysical instinct: you are motivated by understanding how things work, not just that they work and you are excited to tell people “it’s this glycine that did the trick!”.
Enjoy working with dynamic, multi-state proteins and/or complex protein assemblies.
Feel joy in collaboration and communicate naturally with experts across disciplines.
Are energized by 0->1 innovations and always on the lookout for creative ways to do better.
Want to be part of a fast-paced team working on frontier scientific and technical problems.

Nice To Haves

Experience with engineering multi-state proteins and/or multi-component protein assemblies.
Experience with protein expression in primary neurons, brain organoids, or brain tissues.
Physical thinking from ultrasound, optical, photo-physical or photochemical projects.
Experience with large-scale screening.
Experience with creative assay development.
Familiarity with machine learning or data-driven design approaches.

Responsibilities

Design and implement biosensor engineering strategies — from rational and computational design to directed evolution and combinatorial libraries — to push the performance frontier of protein-based biosensors.
Build and characterize protein variants at high throughput to test and iterate hypotheses rapidly and rigorously: implement and optimize scalable workflows from library generation, expression in in vitro systems such as mammalian cell lines, to standard and custom assays.
Collaborate with our machine learning team to establish models linking sequences and structures to function, guide the generation of future constructs, and augment our intuition.
Explore the mechanisms behind complex biomolecules and translate that understanding into creative, out-of-the-box designs (with the support of our biochemists, structural biologists, and computational teams).
Develop novel screening and validation methods to further accelerate the entire design-build-iterate loop and ensure a smooth transition toward in vivo applications.