Senior Deep Learning Engineer

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) 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