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About the Role: We're looking for an experienced Machine Learning Engineer to join our AI team. You'll bridge the gap between research and engineering, implementing and deploying state-of-the-art ASR solutions while maintaining high engineering standards.
Tasks
- Implement, benchmark and deploy state-of-the-art models in speech recognition and audio processing
- Collect and curate custom ASR datasets, including data sourcing, annotation pipeline setup, quality control, and alignment/segmentation procedures
- Ensure continuous training for models on production
- Design and conduct experiments to validate new approaches, datasets, and architectures
- Build and maintain data pipelines and audio preprocessing workflows
- Improve and ensure company follow best MLOps practices
Requirements
Required Qualifications:
- Master’s degree in Computer Science, Engineering, or a related technical field or equivalent industry experience
- 8+ years of experience in ML engineering or relevant fields
- Strong programming skills in Python and ML frameworks (PyTorch, TensorFlow)
- Experience with deep learning models, including transformers
- Experience with MLOps pipeline implementation and maintenance (Docker, MLflow, W&B, DVC, Kubernetes)
Highly Valued:
- Direct experience with ASR models (e.g., Whisper, wav2vec, HuBERT) and speech/audio processing pipelines
- Experience working with multimodal data (e.g., audio + text, audio + video)
- Demonstrated research experience (publications, research projects, or industrial research)
- A hands-on mindset and willingness to engage with meticulous data-related tasks
- Experience with distributed training systems
Nice to Have:
- NLP experience
- Open-source contributions
- Experience in a startup-like environment
Benefits
- Attractive compensation aligned with your skills
- Flexible work arrangements
- Professional development allowance
If you're passionate about Machine Learing and want to work with cutting-edge technologies, we'd love to hear from you!
Location: Hamburg Employment Type: Full-time
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