Founding AI Engineer - Transformer Foundation Models (From Scratch) | Machine Learning | Artificial Intelligence | ML

https://forms.gle/suLFDWfFEshCha7G7

About Us

audio foundation models

The Role

founding AI engineer

What You'll Build:

• Audio Transformer Architectures

  : Design and implement encoder-decoder and decoder-only transformer models specifically for audio processing, including self-attention mechanisms optimized for sequential audio data. 

• Foundation Model Training

  : Train large-scale audio foundation models (100M+ parameters) on diverse unlabelled audio datasets using self-supervised learning objectives like contrastive learning and masked prediction. 

• Distributed Training Infrastructure

  : Implement multi-GPU/TPU training pipelines with model parallelism, gradient checkpointing, and mixed precision for training foundation models at scale. 

• Real-time Inference Systems

  : Deploy foundation models for low-latency audio processing with optimized serving infrastructure, quantization, and caching. 

Must-Have Experience

• Transformer Architecture Expertise

  : Proven experience implementing transformer models from scratch (not using pre-built PyTorch/TensorFlow transformer classes) with deep understanding of attention mechanisms, positional encoding, and layer normalization. 

• Audio Foundation Model Training

  : Direct experience training large neural networks on audio data (speech, music, or environmental sounds) from scratch, including dataset curation and training objective design. 

• Large-Scale Model Training

  : Hands-on experience with distributed training, managing training runs spanning weeks, hyperparameter optimization, and debugging convergence issues with models containing millions of parameters. 

• Audio Signal Processing

  : Strong background in digital audio processing, understanding of sampling rates, spectrograms, mel-frequency analysis, and audio feature extraction methods. 

• Deep Learning Frameworks

  : Expert-level proficiency in PyTorch or JAX with experience in custom model architectures, loss functions, and training loops. 

Preferred Experience

• Self-Supervised Learning

  : Experience with contrastive learning, masked language modeling adapted for audio, or other unsupervised training objectives for foundation models. 

• Audio Applications

  : Background in automatic speech recognition, text-to-speech synthesis, audio generation, or speech understanding tasks. 

• Production Systems

  : Experience deploying large models in production with considerations for latency, throughput, and cost optimization. 

• Research Background

  : Publications or demonstrable research experience in transformer architectures, foundation models, or audio machine learning. 

Role Details

• Title

  : Founding AI/ML Engineer (Co-Founder) 

• Focus

  : Transformer-based audio foundation models built from scratch (no fine-tuning of existing open-source models) 

• Location

  : Remote  

• Type

  : Full-time, founding team member 

What’s on the table:

• 🛠

  Early Builder Role

  – Shape Bharat’s first at-scale AI-led audio ecosystem from the ground up.
• 🤝

  Founding-Level Trust

  – Work directly with the founder and core team; your voice matters in every decision.
• 📈

  Equity Ownership

  – Significant stake in the company (will discuss over the call).
• 🎯

  Freedom to Create

  – Architect AI models, pipelines, and infrastructure without bureaucracy.
• 🌏

  Impact at Bharat-Scale

  – Build for 150+ crore voices, across 22+ languages & dialects, preserving cultural memory.
• 🚀

  Growth & Visibility

  – Recognition in the ecosystem, conferences, and open innovation circles.
• 💡

  End-to-End Ownership

  – From research to deployment, you’ll see your work go live and scale.
• ❤️

  Mission-Driven Work

  – Your AI will directly empower rural creators, storytellers, and everyday citizens.

Ready to build Bharat’s audio future?

https://forms.gle/suLFDWfFEshCha7G7