ML AI Intern

About JoshTalks AI

At JoshTalks AI, we believe voice will become the primary interface between humans and machines. We build benchmarks, datasets, and AI systems that power some of the world's most widely used speech models. Our mission:

• Enable machines to communicate as naturally as humans
• Build benchmarks and datasets that form the backbone of global speech AI progress
• Drive improvements through high-quality, diverse, real-world data — not just compute

What You'll Work On

This is not a conventional internship. You will directly contribute to work that influences the global speech AI ecosystem.

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)

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)