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 Learning 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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