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](http://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:**
* 4+ 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](http://B.Tech) from top-tier engineering colleges
### **Highly Valued:**
* Experience with model serving frameworks (TorchServe, Triton, Ray Serve, vLLM)
* Knowledge of efficient inference techniques (ONNX, TensorRT, quantization)
* Contributions to open-source ML projects
* Experience with vision-language models and document understanding
* Familiarity with LLM fine-tuning techniques (LoRA, QLoRA, PEFT)
## **Why This Role is Exceptional**
* **Proven Impact**: Our models approaching **1 million downloads** – your work will have global reach
* **Real Scale**: Your models will process millions of documents daily for Fortune 500 companies
* **Well-Funded Innovation**: $40M+ in funding means significant GPU resources and freedom to experiment
* **Open Source Leadership**: Publish your work and contribute to models already trusted by nearly a million developers
* **Research-Driven Culture**: Regular paper reading sessions, collaboration with research community
* **Rapid Growth**: Strong financial backing and Series B momentum mean ambitious projects and fast career progression
## **Our Recent Achievements**
* **Nanonets-OCR model: \~1 million downloads on Hugging Face** – one of the most adopted document AI models globally
* Launched industry-first Automation Benchmark defining new standards for AI reliability
* Published research recognized by leading AI researchers
* Built agentic OCR systems that reason and adapt, not just extract
* Secured $40M+ in total funding from Accel, Elevation Capital, and Y Combinator
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Visa sponsorship is not available for this role.
