Nanonets is automating document information extraction using AI. We are headquartered in San Francisco. We are backed by prestigious investors from bay area like Y-Combinator, SV Angels, Sound Ventures by Ashton Kutcher. We are currently profitable and growing at a fast pace and looking to expand our team.
We are building a product that lets companies automate extracting key information from documents like invoices, receipts, or any other kind of document and integrate it into their workflows saving manual work. We need to keep building features that will let users automate millions of documents of different kinds every day, feed them to our AI for learning, plug our API to external systems like salesforce, quickbooks, RPA providers etc.
You should check it out at https://app.nanonets.com
About Nanonets
At Nanonets, we’re building the future of document intelligence. Our platform helps thousands of companies automate workflows that rely on messy, unstructured data—think invoices, PDFs, emails, websites, support tickets—using cutting-edge AI.
We’re growing fast and backed by top investors. Now, we’re looking to build entirely new business lines. That’s where you come in.
What You’ll Do
This is not your typical role. You’ll be given white space, resources, and a mandate:
Identify a high-potential vertical. Validate it. Build it. Own it.
Expect to:
You’d Be a Great Fit If:
Why Join Nanonets as an EIR?
Compensation & Benefits:
$140,000–$180,000 base salary
Equity
Flexible hybrid setup from Palo Alto HQ
Health, dental, vision
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
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 Our stack:
Databases
Cassandra DB
Postgres/MySQL
Backend
Golang for API and other microservices
Python for Machine learning (Tensorflow, Pytorch)
Frontend
React, Typescript
Mobx
Cloud Providers
AWS
GCP for ML heavy workload
Monitoring/Alerting
ELK for logging
Prometheus for Monitoring
Graphana for dashboards
Orchestration
Kubernetes
DevOps
Jenkins for CI/CD