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.
What sets you up for success
Audio research experience: A strong background in speech, audio understanding, audio generation, speech-to-speech systems, or representation learning.
Applied research mindset: You balance scientific novelty with usefulness and measurable impact.
Deep learning proficiency: Hands-on experience with PyTorch, modern model architectures, and large-scale training.
Research breadth: You are comfortable working across architecture, data, evaluation, and infrastructure.
Experimental rigor: You design informative experiments, choose meaningful metrics, and interpret results carefully.
Engineering ability: You write clean Python and can move beyond notebooks into maintainable systems.
Relevant background: MSc, PhD, or equivalent practical experience in machine learning, speech processing, audio, or a related field.
AI-native workflow: You use modern research and coding tools to accelerate exploration, implementation, and analysis.
Beyond your CV
Broadly curious: You are willing to follow the problem across disciplinary boundaries.
Pragmatic: You know when a simple baseline is more informative than a complicated model.
Impact-oriented: You want research to reach users, not stop at a benchmark.
Collaborative: You enjoy working with researchers, engineers, therapists, and product teams.
Self-directed: You can identify the next useful experiment and make it happen.
Why nyra labs
Access to difficult, clinically grounded speech data
The freedom to work across the audio research stack
The opportunity to publish models, datasets, and benchmarks openly
A direct path from research to real-world use
Close collaboration with a small, ambitious team
Attractive compensation, Phantom Stock Options, and company benefits
A beautiful office in Vienna’s First District with a hybrid working model
To apply
Please include:
Your resume
A recent paper in audio or speech research that you liked, plus a short explanation of why it matters
A link to your GitHub, if available
A link to your Google Scholar profile, if available
The process
Intro call, approximately 30 minutes: Your background, expectations, and an introduction to nyra labs.
Research deep-dive: A discussion of previous works, your approach to an audio research problem, and how you would evaluate it.
Meet the founders and team: Discuss research direction, collaboration, and what you would want to explore at nyra labs.