About Nanonets:

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 an API 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

Job Description

The primary role is to solve challenging machine learning problems in the document information extraction space. You are expected to build SOTA models and continue improving our existing SOTA models. The job involves pushing the boundaries of our machine learning capabilities in extracting fields and tables from documents. 
Extracting accurate information from completely new document types
Transferring learning on new labels/table types with few examples
Building robustness against noisy annotations
Generalizing to many formats from a few formats
These are the few examples of exciting problems we are solving here. Your responsibilities include problem designing, annotation designing, annotation management, model development, model training, optimization for prediction, model deployment, continuous improvement, and so on.

What we expect from you:
Strong command in probability and statistics
Strong Machine Learning concepts
Strong programming skills
Experience in Deep Learning projects end-to-end from problem design, annotation quality management, and custom DL architectures to model deployment
Experience with the latest semi-supervised, unsupervised and few shot methods in Deep Learning in CV and/or NLP
Strong command in low-level operations involved in building architectures like Transformers, Efficientnet, ViT, Faster-rcnn, etc., and experience in implementing those in pytorch/jax/tensorflow
Some of the interesting things our senior DL engineers have shipped:
Building SOTA field and table extractors for invoices, receipts, passports, driving licenses, etc.
Fast and accurate OCR Engine deployed on customer infrastructure; used by some big clients
Hierarchical information extraction from documents. Robust modeling for the tree-like structure of sections inside sections in documents. Hierarchical information extraction from Menus
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
Employee Benefits:
Top 10 percentile of the pay (+ Equivalent stock options with 1:1 split)
Open performance appraisal policy (Based on Exceptional performance in KRAs)
Multiple performance appraisals
Unlimited leave policy (depending on the reason for leaves)
Monthly workation - Any office location
Destinations in and around the city
Credit Card given for Office use and getting an office space in the candidate's city or workation destinations
International offsite (with the entire team)

Round	Type	Description
1	Intro Round	30 minute screening call with CTO
2	Technical Round	Technical round for 60 minutes
3	Technical Round	Technical round for Live coding assessment for 60 minutes depending on the panel and candidate's profile
4	Technical Round	Technical round based on product development and programming for 60 minutes
5	Discussion Round	Final round with a mix of technical, cultural fit, and soft skills for 60 minutes

Senior Deep Learning Engineer

Skills Required

Perks and Benefits at Nanonets

Interview Process

Investors