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 genuinely challenging problems at the intersection of computer vision, NLP, and generative AI.

Here's a quick 1-minute intro video .

About the role

The role can be summed up as building and deploying cutting edge generalised deep learning architectures that can solve complex business problems like converting unstructured data into structured format without hand-tuning features/models. You are expected to build state of the art models that are best in the world for solving these problems, continuously experimenting and incorporating new advancements in the field into these architectures.

What were looking for

• 5-8 years of experience in Deep Learning.
• Strong foundational knowledge in deep learning concepts and architectures (LLMs and VLMs)
• Demonstrated expertise in at least one specialised area of deep learning (NLP, computer vision, multimodal models, etc.)
• Experience building and deploying production-grade Deep Learning systems at scale,
• Familiarity with various large language models (GPT, LLaMA, Claude, etc.) and their applications
• Strong software engineering practices including version control, CI/CD, and code quality
• Ability to rapidly learn and apply new technologies and approaches.

Interesting Projects Other Senior DL Engineers Have Completed

• Deployed large scale multi-modal architectures that can understand both text and images really well.
• Built an auto-ML platform that can automatically select the best architecture, fine-tuning method based on type and amount of data.
• Best in the world models to process documents like invoices, receipts, passports, driving licenses, etc.
• Hierarchical information extraction from documents. Robust modeling for the tree-like structure of sections inside sections in documents.
• Extracting complex tables wrapped around tables, multiple fields in a single column, cells spanning multiple columns, tables in warped images, etc.
• Enabling few-shots learning by SOTA finetuning techniques.