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The role involves researching and developing audio models for speech understanding and generation, moving from scientific exploration to production systems. Responsibilities include optimizing model architectures, curating data pipelines, and publishing research findings.
Candidates need a strong background in audio/speech research and proficiency in PyTorch and Python. An MSc, PhD, or equivalent practical experience in machine learning or speech processing is required.
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.
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.
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.
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.
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.
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.
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
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
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.
Pioneering the future of neurotherapy
nyra health was founded with the idea of harnessing the power of AI to overcome limitations of current neurotherapy, expand therapy content, and enable patients to achieve their full therapy potential. By combining artificial intelligence and evidence-based neurological research in an easy-to-use therapy software, we offer a holistic form of therapy at the cutting edge of science. Combined with our second product, nyra insights, nyra health offers an unprecedented view of the patient's progress starting with the moment of diagnosis and accompanying them on every step of the patient journey.
Offices: Wien , Wien 1010, AT
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