With Sonia, doctors are successful doctors. We create and deploy AI-enhanced solutions that make doctors’ lives easier, patients’ care better, and healthcare systems more efficient. If you’re an intrinsically motivated self-starter who values impactful work, join us in revolutionizing healthcare.

We’re looking for a Machine Learning Engineer (ASR) to join our AI team. You will be at the forefront of developing the speech recognition systems that power real-world applications used by clinicians every day.

Location: Preferably Hybrid (Luxembourg or Berlin) or Remote (Germany or Luxembourg).

• ASR Model Development: Fine-tune, evaluate, and deploy state-of-the-art models in speech recognition and audio processing (e.g., Whisper, wav2vec).
• Data Curation & Annotation: Collect and curate custom ASR datasets, including data sourcing, annotation pipeline setup, quality control, and alignment/segmentation procedures.
• Audio Pipelines: Build and maintain robust data pipelines and audio preprocessing workflows for clinical environments.
• MLOps Collaboration: Work closely with the MLOps team to ensure continuous training, monitoring, and seamless deployment of models in production.
• Experimentation: Design and conduct experiments to validate new approaches, datasets, and architectures to improve accuracy in noisy or specialized medical settings.
• Cross-Functional Impact: Collaborate with product managers and developers to translate complex speech solutions into production-ready healthcare tools.

• Education: Master’s degree in Computer Science or Engineering.
• Experience: Typically 5-8+ years of experience in ML engineering.
• Technical Proficiency: Strong programming skills in Python ( working with production code and deploying models in production) and ML frameworks ( PyTorch , TensorFlow or Jax).
• ASR Expertise: Direct experience with ASR models (Whisper, wav2vec, …), VAD, alignment and diarisation, and complex speech/audio processing pipelines.
• Deep Learning: Extensive experience with transformer-based architectures and deep learning models.
• MLOps Foundation: Practical experience with MLOps pipeline components such as Docker, MLflow, W&B, DVC, or Kubernetes .

• Domain Knowledge: Knowledge of multilingual or domain-specific modeling (specifically medical , legal, or other specialized terminologies).
• Scalability: Experience with distributed training systems for large-scale model optimization.
• Language Skills: Full proficiency in English and German.

• Full ownership of impactful ML components in a fast-growing AI environment.
• A collaborative team that values curiosity, learning, and pragmatic problem-solving.
• Flexible working arrangements (remote or hybrid).
• 30 days of annual vacation.
• Competitive salary depending on experience.
• The chance to work on products that directly shape the future of healthcare.