Job Opportunities API

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. 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 researc