Deep Learning (GNN) Engineer

Passionate about developing cutting edge AI solutions that will transform the global construction Industry?

We are building the first NO-CODE AI PLATFORM for the AEC industry, with the 3rd generation AI, that allows users to create complex use cases with zero coding at the frontend and neuro-symbolic AI under the hood.

As a part of this platform, we are working on a module that involves the use of neural reasoning.

Tasks

We are looking for R&D minded Deep Learning Engineer, with demonstrable experience in Graph Neural Networks. The candidates should also have a solid background in Bayesian Learning. With this experience in both classical and deep learning approaches, the candidate will be in a position to create cutting edge "Neural Reasoning" solutions to understand construction sites from sensor data.

The candidate should also have "Deep Learning" experience in Computer Vision topics such as Object Detection, Instance Segmentation, Semantic Segmentation, Pose Estimation, Depth Estimation etc. Experience in more advanced learning methods such as Self-Supervision or Few Shot Learning would be a plus.

The candidate will be required to demonstrate the prototype on real data in real world conditions. We will provide the environment and tools through our platform to productionize it. We expect the candidate to have a balance of innovation and application perspectives. We will provide a unique opportunity to work on the 3rd generation AI and facilitate the candidate to make cutting edge contributions in the form of a product.

You will work closely with and learn from the Founder/CTO, Dr. Krishna Sridhar, former Program Head of Autonomous AI (autonomous driving) at Continental AG and who earlier worked as a Post-Doc AI Researcher at a DARPA Project(US Dept. of Defense).

Requirements

• R&D minded PhD in Deep Learning with 5+ years of experience
• 2+ years of experience with Graph Neural Networks
• Experience in Computer Vision topics such as Object Detection, Instance Segmentation, Semantic Segmentation, Pose Estimation.
• Masters degree in Mathematics/Physics/Theoritical Computer Science from a leading university
• Publications on development and application of GNNs in top conferences (ICCV, CVPR, ICML)
• (Preferred) Experience with Natural Language Processing using Deep Learning
• Implementation experience of developing new deep learning models in PyTorch or TensorFlow
• Evidence of being able to code from scratch in GitHub
• A thorough understanding of software design
• Good communication skills
• Team player
• Fluent English

Benefits

Competitive salary + ESOPs