About the role
As an Applied Researcher in Audio, you will turn promising research into models that work outside the lab.
You will contribute across model architecture, data, training, evaluation, and inference. Depending on the problem, your work could involve speech understanding, generation, representation learning, alignment, multilingual modeling, or multimodal systems.
This role is deliberately broad. We are looking for someone who can move between scientific exploration and practical implementation, then carry a successful experiment through to an open release or production system.
Please note: While we are currently accepting applications for this role, we are not actively hiring for it at the moment. We’ll review relevant profiles and reach out should a suitable opportunity arise.
Why we need you
Audio contains much more than the words in a transcript. Timing, prosody, speaker identity, pronunciation, repairs, vocal events, and acoustic context all carry information.
Most speech systems simplify these details away. That makes them easier to train, but less useful in real communication and especially in neurological care.
nyra labs works on models that preserve and understand more of the original signal. We need an applied researcher who can connect new research ideas with difficult real-world data, rigorous evaluation, and systems that people can actually use.
About the company
At nyra health, we build software that supports clinics, therapists, and patients throughout neurorehabilitation. myReha delivers personalized therapy, while nyra insights helps clinical teams manage and understand patient progress.
nyra labs is the research arm of nyra health. We turn difficult problems encountered in practice into open models, datasets, benchmarks, and research that the wider community can build on.
If that resonates with you, we would love to hear from you.
What you’ll shape
- Audio models: Research and develop models for speech understanding, generation, alignment, representation learning, and related areas.
- Model architecture: Explore architectures that can reason across audio, text, timing, and other relevant signals.
- Data strategy: Curate training mixtures, improve annotation methods, and develop synthetic or model-assisted data pipelines.
- Evaluation: Establish benchmarks that measure the details conventional audio metrics miss.
- Research prototyping: Move quickly from papers and hypotheses to working experiments and clear conclusions.
- Scaling and optimization: Train and optimize models efficiently across modern GPU infrastructure.
- Research to release: Work with engineering to turn successful prototypes into reliable open models and nyra health capabilities.
- Publication: Contribute to papers, technical reports, datasets, and open-source releases.