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Deep Learning Engineer
3 months ago
Machine Learning (ML) is no longer a futuristic concept—it's a present-day powerhouse transforming the business landscape.
Nanonets is at the forefront of this transformation, offering innovative ML solutions designed to make document related processes faster than ever before.
From automating data extraction processes to enhancing reconciliation, our solutions are designed to revolutionize workflows, optimize operations, and unlock untapped potential for our clients.
Our client footprint spans across brands such as Toyota, Boston Scientific, and Entergy to name a few enabling businesses across a myriad of industries to unlock the potential of their visual and textual dataHere's a quick 1-minute intro video.
We recently announced a series B round of $29 million in funding by Accel and are backed by the likes of existing investors including Elevation Capital & YCombinator.
This infusion of capital underscores our commitment to driving innovation and expanding our reach in delivering cutting-edge AI solutions to businesses worldwide.
Read about the release here:
on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity.
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 we're looking forBachelors/Masters degree from a Tier 1 school.
3+ Years of experience in a similar role.
Strong Machine Learning concepts.
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.
Experience with the latest semi-supervised, unsupervised and few shot architectures in Deep Learning methods in NLP/CV domain.Strong command in probability and statistics.
Strong programming skills.
Have previously shipped something of significance, either implemented some paper or made significant changes in an existing architecture etc.
Ideal candidate should have the following skillset
Python
Tensorflow
Experience building and deploying systems
Experience with Theano/Torch/Caffe/Keras all useful
Experience Data warehousing/storage/management would be a plus
Experience writing production software would be a plus
The ideal candidate should have developed their own DL architectures apart from using open source architectures.
Ideal candidate would have extensive experience with computer vision applications.
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.