About the role
Join Nanonets to push the boundaries of what's possible with deep learning. We're not just implementing models – we're setting new benchmarks in document AI, with our open-source models achieving nearly 1 million downloads on Hugging Face and recognition from global AI leaders.
Backed by $40M+ in total funding including our recent $29M Series B from Accel, alongside Elevation Capital and Y Combinator, we're scaling our deep learning capabilities to serve enterprise clients including Toyota, Boston Scientific, and Bill.com. You'll work on challenging problems at the intersection of computer vision, NLP, and generative AI.
What You'll Build
Core Technical Challenges:
- Train & Fine-tune SOTA Architectures: Adapt and optimize transformer-based models, vision-language models, and custom architectures for document understanding at scale
- Production ML Infrastructure: Design high-performance serving systems handling millions of requests daily using frameworks like TorchServe, Triton Inference Server, and vLLM
- Agentic AI Systems: Build reasoning-capable OCR that goes beyond extraction – models that understand context, chain operations, and provide confidence-grounded outputs
Optimization at Scale: Implement quantization, distillation, and hardware acceleration techniques to achieve fast inference while maintaining accuracy
- Multi-modal Innovation: Tackle alignment challenges between vision and language models, reduce hallucinations, and improve cross-modal understanding using techniques like RLHF and PEFT
Engineering Responsibilities:
- Design distributed training pipelines for models with billions of parameters using PyTorch FSDP/DeepSpeed
