Lead ML for Generalizable Subvocal Speech

Empathy Talent; San Francisco (CA); Hybrid; USD 120,000 - 200,000; Full time

Job summary

Empathy Talent in San Francisco is seeking a Machine Learning Engineer to own a challenging project: turning noisy physiological signals into a robust, continuous language interface. You will lead data collection, signal representation, and evaluation with founders and hardware leads.

You will develop generalizable representations from unlabeled data and build sequence models that generalize across users and devices, with ~15 minutes of calibration data.

Qualifications

• Deep experience applying ML to noisy, distribution-shift problems.
• Backgrounds may include: ASR / Speech ML, Audio and speech modeling, Time-series modeling, Brain-Computer Interfaces (BCI), Radar or sensor-based ML, Multimodal or physiological signal processing.
• PhD-level research ability preferred.
• Production-quality code, ability to design experiments independently, and adapt quickly to changing directions.

Responsibilities

• Lead end-to-end ML efforts, collaborating with founders and sensing and hardware leads to define data collection, signal representation, model families, and evaluation.
• Learn generalizable representations from large amounts of unlabeled and weakly labeled physiological time-series data.
• Build continuous sequence models using approaches such as Conformers, Mamba-style architectures, CTC, transducers, and pretrained speech/language models.
• Adapt large cross-user models to new users using approximately 15 minutes of calibration data.
• Design rigorous user-held-out, session-held-out, and device-held-out evaluation frameworks.
• Scale model training across thousands of hours of data and thousands of users.
• Optimize the complete ML system for continuous, low-latency, real-time operation.
• Help define the broader ML architecture, research direction, and technical roadmap as the team scales.

Job description

Empathy Talent in San Francisco is seeking a Machine Learning Engineer to own a challenging project: turning noisy physiological signals into a robust, continuous language interface. You will lead data collection, signal representation, and evaluation with founders and hardware leads.

You will develop generalizable representations from unlabeled data and build sequence models that generalize across users and devices, with ~15 minutes of calibration data.