The Subvocal Company Verified
Artificial Intelligence
Founding Machine Learning Engineer
San Francisco, CAOnsiteFull Time$150,000–$200,000 /yrPosted 2 months agoHidden Gem · YC Startup
Required skills
- generalization
- physiological signals
- continuous language
- subvocal speech
- unlabeled data
- weakly labeled data
- time-series data
- sequence models
- Conformers
- Mamba-style architectures
Nice to have
- radar
- neuroscience
- BCIs
Role summary
Subvocal is seeking a Founding Machine Learning Engineer to own the core ML problem of transforming noisy physiological signals into continuous language. This role involves leading end-to-end efforts in generalization, model development, and evaluation, working closely with founders and hardware leads. Responsibilities include learning representations from time-series data, building sequence models (e.g., Conformers, Mamba), separating signal variations, adapting models with limited calibration data, designing robust evaluations, scaling training, and ensuring low-latency real-time performance. The ideal candidate has strong deep learning experience in speech, time series, or biosignals, and can write production-quality code.
We are looking for the person who will own the central machine learning problem at Subvocal: turning weak, noisy, highly variable physiological signals into continuous language.
We have already built prototypes that decode subvocal speech at more than 200 words per minute. The much harder problem now is generalization. A model that works on one person, in one session, with one device placement is not a product. It needs to work when the same person returns the next day, when the hardware moves slightly, and eventually when a completely new person puts it on and gives us only a few minutes of calibration data.
You will lead that effort end to end. You will work directly with the founders and our sensing and hardware leads to decide what data we collect, how we represent the signal, which model families we pursue, and how we evaluate whether we are actually making progress.
Some of the problems you will work on include:
* Learning general representations from large amounts of unlabeled and weakly labeled physiological time-series data.
* Building continuous sequence models using approaches such as Conformers, Mamba-style architectures, CTC, transducers, and pretrained speech or language models.
* Separating speech-related information from anatomy, placement, session, device, and environmental variation.
* Adapting a large cross-user model to a new person from roughly 15 minutes of calibration data.
* Designing honest user-held-out, session-held-out, and device-held-out evaluations.
* Scaling training across thousands of hours and thousands of people.
* Getting the complete system to run continuously with low enough latency for real-time use.
Our current ML stack is primarily Python, PyTorch, CUDA, and distributed GPU training, with custom infrastructure for signal processing, data collection, experiment tracking, and evaluation.
You might be a great fit if you have unusually strong experience in deep learning for speech (ASR), time series, biosignals, neuroscience, BCIs, radar, or another domain where signals are noisy and data distributions shift constantly. We are especially interested in people with PhD-level research ability, whether or not that came through a formal PhD, who are also comfortable writing production-quality code and moving quickly when the research direction changes.
This is not a role where you will be handed a model architecture and asked to improve it incrementally. You will help decide how the problem should be framed in the first place, build the initial ML organization around you, and directly determine whether this technology becomes a real product.
This is a full-time, in-person role in San Francisco.
We have already built prototypes that decode subvocal speech at more than 200 words per minute. The much harder problem now is generalization. A model that works on one person, in one session, with one device placement is not a product. It needs to work when the same person returns the next day, when the hardware moves slightly, and eventually when a completely new person puts it on and gives us only a few minutes of calibration data.
You will lead that effort end to end. You will work directly with the founders and our sensing and hardware leads to decide what data we collect, how we represent the signal, which model families we pursue, and how we evaluate whether we are actually making progress.
Some of the problems you will work on include:
* Learning general representations from large amounts of unlabeled and weakly labeled physiological time-series data.
* Building continuous sequence models using approaches such as Conformers, Mamba-style architectures, CTC, transducers, and pretrained speech or language models.
* Separating speech-related information from anatomy, placement, session, device, and environmental variation.
* Adapting a large cross-user model to a new person from roughly 15 minutes of calibration data.
* Designing honest user-held-out, session-held-out, and device-held-out evaluations.
* Scaling training across thousands of hours and thousands of people.
* Getting the complete system to run continuously with low enough latency for real-time use.
Our current ML stack is primarily Python, PyTorch, CUDA, and distributed GPU training, with custom infrastructure for signal processing, data collection, experiment tracking, and evaluation.
You might be a great fit if you have unusually strong experience in deep learning for speech (ASR), time series, biosignals, neuroscience, BCIs, radar, or another domain where signals are noisy and data distributions shift constantly. We are especially interested in people with PhD-level research ability, whether or not that came through a formal PhD, who are also comfortable writing production-quality code and moving quickly when the research direction changes.
This is not a role where you will be handed a model architecture and asked to improve it incrementally. You will help decide how the problem should be framed in the first place, build the initial ML organization around you, and directly determine whether this technology becomes a real product.
This is a full-time, in-person role in San Francisco.
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