AI-ML Intern

About the Role

AI-ML Intern at Voice Arena

Key Responsibilities

Data Delivery (Cloud & Containers)

• Deliver processed speech and audio datasets via cloud-based container pipelines (Docker, Kubernetes)
• Design and manage end-to-end data delivery workflows from raw ingestion to structured output
• Build and maintain ETL pipelines to prepare data for model training and evaluation at scale
• Ensure reliable, versioned, and reproducible data handoffs to ML training infrastructure
• Monitor pipeline health, resolve bottlenecks, and optimize throughput for large-scale data movement

Benchmarking & Evaluation

• Design and run evaluations for ASR and speech-to-speech systems
• Benchmark leading speech models to identify real-world strengths, weaknesses, and failure modes
• Build evaluation frameworks that guide top AI labs on model performance and improvement areas

Modeling & Fine-Tuning

• Fine-tune speech recognition models (e.g., Whisper, wav2vec2) targeting Word Error Rates of ~5%
• Experiment with multilingual, code-switched, accented, and noisy speech data
• Work with large-scale, production-grade speech datasets across 20+ Indian languages

Requirements & Tech Stack

• Students in B.Tech/B.E. graduating in 2027 (CSE, EE, AI/ML, or related fields)
• Strong interest in speech, audio, NLP, or multimodal AI
• Hands-on experience in one or more of the following:
• Fine-tuning speech or language models (Whisper, wav2vec2, HuBERT, etc.)
• Building speech-driven pipelines, classifiers, or assistants
• Working with PyTorch, TensorFlow, or Hugging Face Transformers
• Cloud platforms (AWS, GCP, Azure) and container tools (Docker, Kubernetes)

Preferred Qualifications

• Open-source contributions, GitHub projects, Kaggle experience
• Experience with multilingual or low-resource speech data
• Familiarity with ETL workflows, data versioning (DVC, MLflow), or orchestration (Airflow, Prefect)