Asks for

The ad says“ASR Expertise: Direct experience with ASR models (Whisper, wav2vec, …), VAD, alignment and diarisation, and complex speech/audio processing pipelines.”
The ad says“ASR Expertise: Direct experience with ASR models (Whisper, wav2vec, …), VAD, alignment and diarisation, and complex speech/audio processing pipelines.”
The ad says“Deep Learning: Extensive experience with transformer-based architectures and deep learning models.”
The ad says“ASR Expertise: Direct experience with ASR models (Whisper, wav2vec, …), VAD, alignment and diarisation, and complex speech/audio processing pipelines.”
The ad says“MLOps Foundation: Practical experience with MLOps pipeline components such as Docker, MLflow, W&B, DVC, or Kubernetes.”
The ad says“MLOps Foundation: Practical experience with MLOps pipeline components such as Docker, MLflow, W&B, DVC, or Kubernetes.”
The ad says“Technical Proficiency: Strong programming skills in Python (working with production code and deploying models in production) and ML frameworks (PyTorch, TensorFlow or Jax).”
The ad says“MLOps Foundation: Practical experience with MLOps pipeline components such as Docker, MLflow, W&B, DVC, or Kubernetes.”
The ad says“MLOps Foundation: Practical experience with MLOps pipeline components such as Docker, MLflow, W&B, DVC, or Kubernetes.”
The ad says“Technical Proficiency: Strong programming skills in Python (working with production code and deploying models in production) and ML frameworks (PyTorch, TensorFlow or Jax).”
The ad says“Technical Proficiency: Strong programming skills in Python (working with production code and deploying models in production) and ML frameworks (PyTorch, TensorFlow or Jax).”
The ad says“Technical Proficiency: Strong programming skills in Python (working with production code and deploying models in production) and ML frameworks (PyTorch, TensorFlow or Jax).”
The ad says“Deep Learning: Extensive experience with transformer-based architectures and deep learning models.”
The ad says“ASR Expertise: Direct experience with ASR models (Whisper, wav2vec, …), VAD, alignment and diarisation, and complex speech/audio processing pipelines.”
The ad says“MLOps Foundation: Practical experience with MLOps pipeline components such as Docker, MLflow, W&B, DVC, or Kubernetes.”

Let me introduce...

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).

This is what you’ll own

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.

You’ll thrive in this role if you bring

Must Haves

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.

Nice to Have

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.

Why you’ll love working with us

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.

Ready to apply?

If you’re passionate about Machine Learning and want to make an impact in healthcare with cutting-edge AI technologies, we’d love to hear from you!

I’m Margarita and will be guiding you through the application process.