Core Responsibilities

1. Design, tune, and evaluate deep learning architectures (e.g., U-Nets, YOLO variants).

2. Explore new modelling approaches and conduct comparative experiments for new challenges

3. Lead data purification efforts: filtering, augmentation, and dataset optimization, have in mind transfer learning.

4. Define success metrics and analyze model performance across tasks.

5. Present findings clearly through visualizations, reports, and internal reviews.

6. Collaborate on deployment strategy by identifying the best algorithmic solutions for real-world constraints.

Qualifications/Requirements:

Required: PhD with 5 years of experience or MS/MTech degree with 8 or more years of Algorithm development experience.

Required: Strong background in deep learning and computer vision.

Required: Hands-on experience with PyTorch and model training workflows.

Required: Ability to communicate complex ideas clearly and lead technical discussions.

Preferred: Experience with experiment tracking tools (e.g., Tensor Board, W&B).

Preferred: Familiarity with cross-platform development and system integration.

Algorithm Engineer