Vision Deep Learning Engineer

Streamingo.ai has developed an AI platform that implements end to end workflow automation for Video AI workflows. We call it https://deeplabel.app .

We are currently looking for bright Deep learning talent who have 3-5 years of industry experience(1.5-2 years in practical Deep Learning industry projects), working on problems related to the Video AI space.

A Deep Learning engineer at Streamingo, reads research papers to understand the latest developments in activity recognition, multi camera object tracking problems, and develops production quality code to convert the research to usable features on https://deeplabel.app. Along with strong research skills, good coding skills and knowledge of Data structures and algorithms is required.

Ideal candidate should have the following job related attributes.

1. Strong coding skills in Python
2. Thorough handson knowledge of Deep Learning frameworks - Pytorch, Onnx, Tensorflow
3. Excellent understanding and experience of deploying data pipelines for Computer Vision projects.
4. Good working knowledge of troubleshooting, fixing and patching Linux system level problems ( we expect engineers to setup their own workstations, install troubleshoot Cuda problems and OpenCV2 problems).
5. Good understanding of Deep Learning concepts (model parameters, tuning of models, optimizers, learning rates, attention mechanisms, masking etc).
6. Thorough understanding of deep learning implementations of activity detection or object detection.
7. Ability to read research papers and implement new approaches for activity detection and object detection.
8. Knowledge of deployment tools like Triton inference server or Ray are a plus.

In addition to the above : we need a few key personality attributes

1. Willingness to learn and try till you succeed
2. Curiosity to learn and experiment.
3. Ability to work with full stack engineers of the AI platform team, to deploy your new innovations.
4. Good communication skills
5. Ability to take ownership of the respective modules for the whole lifecycle till deployment.