AI Intern

About the Role

As an AI Intern at Veena, you’ll be the bridge between music creation and machine learning. Your daily work will involve building robust data pipelines, curating high‑quality audio datasets, and designing agentic LLM workflows that power the AI layer of our digital audio workstation.

Key Responsibilities

• Design and implement end‑to‑end data pipelines that ingest raw audio files, perform format normalization, silence trimming, and feature extraction, and output ready‑to‑train tensors.
• Curate and maintain large, balanced datasets for in‑house models, ensuring label accuracy, class balance, and edge‑case coverage.
• Develop and run multi‑step LLM pipelines that orchestrate tool calls, perform evaluations, and handle error recovery to keep workflows reliable.
• Collaborate with the engineering and music teams to define data quality metrics and automate quality checks for audio metadata and content.
• Experiment with audio ML libraries (e.g., librosa, torchaudio) and embed audio embeddings into downstream models.
• Document pipeline architecture, data schemas, and best practices for future team members.
• Iterate on feedback loops by listening to generated audio and refining preprocessing steps to improve model outputs.

Skills & Qualifications

• Python programming with experience in data manipulation libraries (pandas, NumPy).
• Hands‑on knowledge of agentic AI or LLM workflow frameworks (LangChain, LlamaIndex, or similar).
• Familiarity with SQL for querying and managing metadata tables.
• Basic audio processing concepts: sample rates, bit depth, loudness normalization, stems vs. one‑shots.
• Understanding of AI training pipelines, embeddings, and evaluation metrics.
• Strong problem‑solving mindset and attention to detail in data curation.
• Optional: personal music production experience or a portfolio of audio‑related projects.

What You’ll Learn

In this internship you’ll gain hands‑on experience building production‑grade data pipelines that handle millions of audio samples, a skill highly sought after in AI and ML roles. You’ll also deepen your understanding of agentic AI, learning how to orchestrate LLMs with external tools and evaluate their outputs in a real‑world setting. The role offers a rare blend of music and technology, giving you a unique perspective that can set you apart in future data‑science or AI positions.

Resume Tip

Showcase a concrete data‑pipeline project in your portfolio: include a brief description, the tech stack, and a link to the code or a demo. Highlight any metrics you improved (e.g., dataset size, label accuracy, or pipeline runtime) to demonstrate tangible impact.