Machine Learning Engineer position for STT/TTS in the Netherlands
Work in Groningen at an AI start-up focusing on speech technology
Attention MSc Voice Tech Alumni: DataQueue is hiring a Machine Learning Engineer for STT/TTS.
Meet DataQueue
DataQueue is AI company in Groningen that offers a no-code AI platform which allows businesses to rapidly create AI models grounded in their own data. Their key offerings include:
Data Curation Platform: Designed for MLOps and data scientists to prepare datasets before annotation and labeling
Labelers Management Platform: Integrates with annotation tools to manage data labeling projects
Speech Analytics Platform: Provides speech analytics for monitoring agent performance and improving customer interactions
Industry-Specific Search Engines: Customizable search capabilities tailored for specific industries
Real-Time Computer Vision Platform: A no-code platform for real-time computer vision applications
ML Engineer Position Overview
We are seeking a skilled Machine Learning Engineer to join our team, focusing on the development, training, and fine-tuning of Speech-to-Text (STT) and Text-to-Speech (TTS) models. The ideal candidate will have strong expertise in speech processing, deep learning, and production ML systems.
Key Responsibilities
Design, train, and optimize state-of-the-art STT and TTS models
Fine-tune existing speech models for specific use cases and languages
Develop and maintain data processing pipelines for speech datasets
Implement efficient training procedures and experiment tracking
Optimize model performance, size, and inference speed
Collaborate with engineering teams to deploy models in production
Research and implement latest advances in speech technology
Monitor and improve model metrics and performance
Required Qualifications
Master's or Ph.D. in Computer Science, Machine Learning, Voice Technology, or related field
Experience in deep learning and speech technologies
Strong expertise in PyTorch or TensorFlow
Proficiency in Python and ML development tools
Strong background in speech recognition and synthesis architectures
Experience with transformers and attention-based models
Knowledge of audio signal processing fundamentals
Preferred Skills
Experience with Wav2Vec, Whisper, FastSpeech, Tacotron, or similar models
Familiarity with ML deployment frameworks (TensorRT, ONNX)
Experience with distributed training systems
Knowledge of various STT/TTS evaluation metrics (WER, BLEU, MOS)
Background in multilingual speech systems
Experience with cloud platforms (AWS, GCP, Azure)
Contributions to open-source speech technology projects
CVs can be sent to my email (mnawahda@dataqueue.ai) along with an application letter. Contact Bashir for more info.
