The roles and responsibilities are :
– Learning about our Machine Learning stack which involves many Computer Vision and inference algorithms. Contributing to the codebase wherever necessary. [30%]
– Training algorithms on client datasets with required accuracy and delivering within time bounds. [20%]
– Research work to increase ParallelDots accuracy in tasks like object detection, self-supervised learning, Bayesian Inference, and Boosting Trees.

Skill sets we are looking for :

– Minimum 3 years experience in the field of Data Science
– PyTorch, OpenCV, and Deep Learning / Computer Vision stack. {Must have}
– Wandb, streamlit, and Docker/Kubernetes. {Good to have}
– PyMC3 for Bayesian Inference and XGBOOST for classification. {Good to have}

Following candidates will be given priority :

– Worked at a university research lab on a Deep Learning project.
– Worked with a team where you contributed to a Computer Vision product {launched} / Kaggle competition {results in top 20%}.
– Real-world Object Detection / Classification work will be given priority.
– Published a paper {applied not review} in Deep Learning or Computer Vision.