Machine Learning Researcher

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., * 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, * 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

Do you have a Master's degree?, * 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