Why we need you

nyra health sits on a unique asset: the world's largest labeled dataset of neurological speech — millions of recordings from patients with aphasia, dysarthria, cognitive-linguistic impairments, and other conditions, annotated by professional therapists in real clinical settings. As an ML Researcher, you'll leverage this data to push the boundaries of what AI can do in neurological rehabilitation — building models for speech recognition, pronunciation assessment, therapy personalization, and clinical decision support that no one else can build.

About the Company

At nyra health, we’re building software that makes a real difference. Our tools help clinics, therapists, and patients in their neurorehabilitation journey. Our products — myReha (therapy app) and nyra insights (web platform for patient management and analysis) — deliver evidence-based, AI-powered neurotherapy at scale.

If that resonates with you, we’d love to hear from you.

What you'll shape

Speech and language models: Develop and improve models for neurological speech recognition, pronunciation scoring, and phoneme-level analysis — building on our proprietary models
Clinical AI: Research and prototype models for therapy content generation, patient progress prediction, and clinical decision support
Multimodal approaches: Explore combinations of speech, text, and behavioral signals to build richer models of patient recovery
Data pipeline and evaluation: Design evaluation frameworks, build training pipelines, and establish rigorous benchmarks for model performance in clinical contexts
Publication and collaboration: Contribute to scientific publications and collaborate with academic partners on research projects
Production integration: Work with Engineering to deploy models into our production stack — ensuring they run reliably at scale on-device and in the cloud
Research direction: Help shape nyra's ML research roadmap, identifying high-impact opportunities at the intersection of AI and neurological rehabilitation

What sets you up for success

AI-native workflow: You use AI tools daily — Claude Code, Cursor, Copilot, or similar — not as a novelty, but as your default way of working. You write better code faster because of it.
Multi-agent fluency: You're comfortable orchestrating multiple AI agents, chaining prompts, and building workflows where AI handles the heavy lifting while you steer the outcome.
Tooling mindset: You actively shape your own dev environment — custom scripts, automation, MCP servers, CLI tools — and you're always looking for ways to remove friction from your workflow.
ML research experience: MSc or PhD (or equivalent practical experience) in machine learning, speech processing, NLP, or a related field
Speech and audio expertise: Strong background in speech recognition, speech synthesis, audio processing, or related areas (experience with Whisper, wav2vec, HuBERT, or similar architectures)
Deep learning proficiency: Hands-on experience with PyTorch, model training at scale, and modern training techniques (LoRA, RLHF, distillation, etc.)
Research rigor: Track record of publications, open-source contributions, or documented research projects
Engineering skills: Ability to write clean, production-quality Python code — not just notebooks
Evaluation mindset: Experience designing experiments, defining metrics, and drawing valid conclusions from data

Beyond your CV

Scientifically rigorous: You care about doing things right — proper evaluation, reproducibility, and honest assessment of what works and what doesn't
Impact-oriented: You're motivated by building models that ship and make a difference, not just benchmarks
Curious and collaborative: You enjoy working with clinicians, therapists, and product teams to understand the problems worth solving
Self-directed: You can identify promising research directions, design experiments, and drive projects forward with minimal guidance
Passionate about speech and language: You find the intersection of AI and human communication fascinating — especially in populations underserved by existing technology

Why nyra health

Access to the world's largest labeled dataset of neurological speech
Fast-growing digital health scale-up with international ambition
Direct collaboration with the founders and cross-functional teams
Opportunity to publish and collaborate with top academic institutions
Responsibility from day one and tangible impact on patient outcomes
Transparent, direct communication and feedback culture
Attractive compensation, Phantom Stock Options, and company perks (Wiener Linien Jahreskarte etc.)
Beautiful office in Vienna's First District with regular team events

To apply:

Submit your resume and include the following:

A link to a recent paper in the audio space you liked and why you think it is significant (mandatory).
A link to your GitHub (optional)
A link to your google scholar (optional)

The Process

Intro call (~30 mins): Background, expectations, and an overview of nyra health and the role.
Technical deep-dive: A research discussion covering your past work, approach to a relevant problem, and a technical scenario, with our ML and Engineering leads.
Meet with Founders: Discuss your research vision, how you'd leverage nyra's data assets, and how you see the future of AI in neurological care.

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