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 . You'll work on genuinely challenging problems at the intersection of computer vision, NLP, and generative AI.Here's a quick 1-minute intro video.What You'll BuildCore Technical Challenges:Train & Fine-tune SOTA Architectures: Adapt and optimize transformer-based models, vision-language models, and custom architectures for document understanding at scaleProduction ML Infrastructure: Design high-performance serving systems handling millions of requests daily using frameworks like TorchServe, Triton Inference Server, and vLLMAgentic AI Systems: Build reasoning-capable OCR that goes beyond extraction – models that understand context, chain operations, and provide confidence-grounded outputsOptimization at Scale: Implement quantization, distillation, and hardware acceleration techniques to achieve fast inference while maintaining accuracyMulti-modal Innovation: Tackle alignment challenges between vision and language models, reduce hallucinations, and improve cross-modal understanding using techniques like RLHF and PEFTEngineering Responsibilities:Design distributed training pipelines for models with billions of parameters using PyTorch FSDP/DeepSpeedBuild comprehensive evaluation frameworks benchmarking against GPT-4V, Claude, and specialized document AI modelsImplement A/B testing infrastructure for gradual model rollouts in productionCreate 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 RequirementsMust-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 optimizationProven track record of taking models from research to productionSolid understanding of transformer architectures, attention mechanisms, and modern training techniques.B.E./B.Tech from top-tier engineering collegesHighly Valued:Experience with model serving frameworks (TorchServe, Triton, Ray Serve, vLLM)Knowledge of efficient inference techniques (ONNX, TensorRT, quantization)Contributions to open-source ML projectsExperience with vision-language models and document understandingFamiliarity with LLM fine-tuning techniques (LoRA, QLoRA, PEFT)Why This Role is ExceptionalProven Impact: Our models approaching 1 million downloads – your work will have global reachReal Scale: Your models will process millions of documents daily for Fortune 500 companiesWell-Funded Innovation: $40M+ in funding means significant GPU resources and freedom to experimentOpen Source Leadership: Publish your work and contribute to models already trusted by nearly a million developersResearch-Driven Culture: Regular paper reading sessions, collaboration with research communityRapid Growth: Strong financial backing and Series B momentum mean ambitious projects and fast career progressionOur Recent AchievementsNanonets-OCR model: ~1 million downloads on Hugging Face – one of the most adopted document AI models globallyLaunched industry-first Automation Benchmark defining new standards for AI reliabilityPublished research recognized by leading AI researchersBuilt agentic OCR systems that reason and adapt, not just extract

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