Nanonets is a startup headquartered in the San Francisco Bay Area, solving
real-world business problems with cutting-edge deep learning. We are backed by
prestigious investors from Silicon Valley, such as Y-Combinator (Sam Altman
was our group partner at YC), SV Angels, and Elevation Capital. Our product
automates complex business processes involving unstructured data, using deep
learning to convert it into a structured format and connect multiple
applications with each other, all in an automated manner. Since 2021, we have
been building and using large-scale multimodal architectures in deep learning,
such as GPT-4, which have gained popularity in recent times. Some of the
recent work we are doing involves using these architectures to automate
building workflows that will completely replace RPA as an industry. If you are
looking to work at a startup with really smart colleagues, working on state-
of-the-art deep learning architectures, solving real-world problems, and have
product-market fit with rapidly growing customers/revenue, Nanonets would be
an ideal place for you! Job Description 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 Expect From You 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 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
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
