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 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./ 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