Senior Deep Learning Engineer

About NanoNets

Automatic Data Extraction

Tech

### Some of the interesting things our backend team has shipped - Compile python code into C which could be imported into golang and then shipped as binary for on premise systems - Autoscale GPU dependent services with kubernetes with a custom metric - Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics - Building large number and variety of integrations with relatively generic interface like salesforce, quickbooks, RPA's, external databases - Process large number of files in highly distributed manner in golang ### Some of the interesting things our frontend team has shipped - Ability for users to annotate documents so AI can learn which fields to extract - Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics - Letting users build complex visual workflows around our API in our product. - Let users visualize complex ML metrics in a very simple and intuitive way Our stack: - Databases - Cassandra DB - Postgres/MySQL - Backend - Golang for API and other microservices - Python for Machine learning (Tensorflow, Pytorch) - Frontend - React, Typescript - Mobx - Cloud Providers - AWS - GCP for ML heavy workload - Monitoring/Alerting - ELK for logging - Prometheus for Monitoring - Graphana for dashboards - Orchestration - Kubernetes - DevOps - Jenkins for CI/CD

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](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 computationWe’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](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##