This is a full-time, in-person role in San Francisco.
At a high level, we are trying to detect the incredibly subtle physiological changes that happen when someone forms words internally, and turn those signals into continuous language. The broader sensing design space includes RF sensing, EMG, EEG, mmWave, and other non-invasive methods. For competitive and IP reasons, we are not publicly disclosing the exact architecture of our current system yet, although we can share much more during the interview process.
The hard part is not just getting a model to work on one person in one recording session. These signals can change when the device moves slightly, when the same person comes back the next day, or when you move to someone with completely different anatomy. We need the system to work across people, devices, and environments, then adapt to a new user from only a few minutes of calibration. Solving that involves a mix of signal processing, self-supervised learning, large temporal models, personalization, and language decoding. We currently experiment with architectures including Conformers, Mamba-style sequence models, and pretrained speech and language models. Most of our ML stack is built in Python and PyTorch, with custom infrastructure for data collection, signal processing, distributed training, and evaluation.
At the same time, we are taking a research system built from laboratory equipment and turning it into a wearable. That means building custom sensing and mixed-signal electronics, embedded and FPGA systems, custom ASICs, high-speed data acquisition, firmware, power systems, and eventually fitting everything into a small wearable. Sensor placement, electrical design, mechanical design, and the model are all tightly connected. Moving something by a few millimeters or changing part of the electronics can alter the data distribution enough to affect the model.
A large part of our next phase is also a data problem. We are building the infrastructure to collect thousands of hours of high-quality subvocal speech data across thousands of people, train models continuously as that dataset grows, and understand exactly how performance scales with more users and more variation.
This is what makes the work unusually interesting. There is no established playbook, and the important breakthroughs can come from a better model, a better sensing configuration, a clever piece of hardware, or simply discovering that we have been framing the problem incorrectly. The people joining now will have room to work across those boundaries and make decisions that directly determine whether the system works.