Role Description
DataQueue is the largest and fastest-growing Voice AI company in the MENA region, deploying AI voice agents across banks, telecoms, and governments — and scaling rapidly.
**VoiceHub**, our platform, enables businesses to design, test, and deploy AI voice agents at scale, combining LLMs with a full voice stack including TTS, STT, copilots, speech analytics, and real-time workflows across 25\+ languages.
We are looking for **Machine Learning Intern** who is passionate about Artificial Intelligence, especially Large Language Models (LLMs), Speech-to-Text (STT), and Text-to-Speech (TTS) technologies.
As an intern, you will work closely with ML engineers to build, test, and improve AI systems that power conversational agents, voice recognition systems, and natural language processing applications.
**Responsibilities:**
* Assist in researching and experimenting with LLM, STT, and TTS models.
* Support model training, fine-tuning, and evaluation under guidance.
* Help prepare and preprocess text and audio datasets.
* Contribute to building training and inference pipelines.
* Test and benchmark models for accuracy and performance.
* Stay updated with the latest advancements in AI and deep learning.
**Requirements:**
* Currently pursuing or recently completed a Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
* Understanding of machine learning and deep learning concepts.
* Familiarity with Python.
* Experience with at least one ML framework (PyTorch or TensorFlow is preferred).
* Knowledge of NLP concepts (tokenization, embeddings, transformers).
* Interest in LLMs (GPT, BERT, etc.) or STT (Whisper, etc.).
* Personal or academic projects involving NLP, speech processing, or LLMs.
**Nice-to-Have Skills:**
* Familiarity with cloud platforms (AWS, GCP, or Azure).
* Basic understanding of Docker or model deployment.
* Exposure to vector databases or Retrieval-Augmented Generation (RAG).
Job Type: Full-time
Pay: €2\.500,00 - €3\.000,00 per month
Work Location: Remote