- 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.