Senior Software Engineer

Overview

Nanonets has a vision to help computers see the world starting with reading and understanding documents. Our product helps businesses automate document related workflows for enterprise office teams such as invoice data entry for AP teams, KYC automation for banks and insurance etc.

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.

Nanonets is proud to be an equal opportunity workplace dedicated to pursuing, hiring, and retaining a diverse workforce.

About the Role

As a Senior fullstack engineer, you should be comfortable with both backend (Preferably Golang) and frontend (Preferably React). You will be primarily working on architecting and shipping new backend features like new integrations, enabling more machine learning API’s, building complex workflows & various growth hacking efforts. You will also work on optimizing response times, building features that will scale to 100’s of millions of documents we process every month.

What we expect from you

• Agility in shipping features
• Good at code design and architecture
• Great communication
• Backend experience - Preferably golang, python
• Databases - Understanding of data modeling for Nosql preferably cassandra.
• Strong fundamentals in OOP design patterns.
• Basic understanding of Devops.
• Curiosity and Willingness to learn new things while solving a challenging problem.
• Learning and incorporating best practices in software development, security and design/architecture.

Some of the interesting features we have shipped in backend

• 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
• Intelligent lookups leveraging vector databases with data synced from ERP Systems

Some of the interesting things we have shipped in frontend -

• 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