Senior Deep Learning Engineer

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

- Build comprehensive evaluation frameworks benchmarking against GPT-4V, Claude, and specialized document AI models

- Implement A/B testing infrastructure for gradual model rollouts in production

- Create reproducible training pipelines with experiment tracking

- Optimize inference costs through dynamic batching, model pruning, and selective computation

We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity.

Technical Requirements

Must-Have:

- 3+ years of hands-on deep learning experience with production deployments

- Strong PyTorch expertise – ability to implement custom architectures, loss functions, and training loops from scratch

- Experience with distributed training and large-scale model optimization

- Proven track record of taking models from research to production

- Solid understanding of transformer architectures, attention mechanisms, and modern training techniques

- B.E./B.Tech from top-tier engineering colleges

Highly Valued:

- Experience with model serving frameworks (TorchServe, Triton, Ray Serve, vLLM)