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

Nanonets is a SAAS product that automates manual processes for other companies. We use state-of-the-art machine learning algorithms to enable this hyper-automation.

We are looking for our first Sales Engineer to work closely with our sales team (In US timezone) and assist them in selling this technical product to other companies. A big part of the role will be to understand Nanonets product really well, understand customer requirements/goals and build machine learning models on the Nanonets platform to demo capabilities to customers on their own data. This would be instrumental in helping close sales faster.

If you are interested in AI/ML space, good with communication, enjoy tasks with quick wins and love solving on diverse set of problems, this role would be good fit for you.

Responsibilities:

1. Accompanying salespeople on client calls to answer technical questions, understand their current processes and goals with Nanonets, and suggest improvements.
2. Building solutions/demos based on their requirements using Nanonets product
3. Solving challenges potential customers might be facing with the product
4. Researching about customers current tools/stack and building technical architecture for end solution we are proposing that will solve their problem

Some of the interesting things our backend team has shipped

• 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

Some of the interesting things our frontend team has shipped

• 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