About Nanonets

Nanonets has a vision to help computers see the world starting with reading and understanding documents.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, Bill.com and Entergy to name a few enabling businesses across a myriad of industries to unlock the potential of their visual and textual data

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:

https://www.forbes.com/sites/davidprosser/2024/03/12/why-enterprises-are-learning-to-love-nanonets-automation/?sh=6d79ec8f3ca1

https://techcrunch.com/2024/03/12/nanonets-funding-accel-india/amp/

We’re 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.

About the role

We are seeking a driven Deep Learning Staff Engineer who thrives working in a fast-paced development environment. You will be part of a team that creates deep learning processes from the concept stage through R&D and to productization. The role can be summed up as building and deploying cutting edge generalized deep learning architectures that can solve complex business problems like converting unstructured data into structured format without hand-tuning features/models.

Responsibilities:

• Build state of the art models to solve complex business problems.
• Lead the research initiatives for the Deep learning projects to continuously experiment and incorporate new advancements in the field into different system architectures, streamlining engineering efforts
• Create highly scalable frameworks for all stages of the DL model lifecycle including data curation, synthetic data creation, training and inference.
• Evaluate the current procedures to identify shortcomings and take steps to address them
• Drive innovative ideas for modelling techniques and performance enhancements.
• Collaborate with infrastructure teams to develop tools and interfaces for quick benchmarking, model analysis and optimization, hyper-parameter tuning, and monitoring
• Take care of different data engineering tasks like defining data requirements, labelling, augmenting etc, modelling tasks like training deep learning models, defining evaluation metrics etc. and deployment tasks like converting prototyped code into production code, setting up a cloud environment to deploy the model etc.

Requirements:

• Bachelor’s degree in Engineering, Computer science, Information Systems or a related field. Masters degree is a plus
• 8+ years of experience in Deep Learning.
• Experience in Python, CUDA, AWS, tensorflow/pytorch etc.
• Very good communication skills
• Strong analytical and problem solving skills
• Experience in NLP (natural Language processing), multi-modal AI like VLM (Vision-Language Models) and LLM(Large language models)
• Very strong research background
• Experience with DL frameworks, building and deploying systems
• Have previously shipped something of significance, either implemented some paper or made significant changes in an existing architecture etc

Some extra insights:

Our Tech Stack

• Databases : Cassandra DB; Postgres/MySQL, Deep learning: Pytorch, tensorflow
• 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 ; Grafana for dashboards
• Orchestration : Kubernetes
• DevOps : Jenkins for CI/CD