Responsibilities
- Develop, train, and validate deep learning models for perception tasks such as object detection, semantic segmentation, and lane detection.
- Contribute to the design and implementation of experiments, performing rigorous analysis of model performance and identifying failure modes.
- Analyze large-scale, unstructured video data to derive insights and assist in curating high-quality datasets for model training.
- Collaborate with the team to build and maintain robust MLOps pipelines for data processing, training, and deployment.
- Implement and optimize algorithms in Python, ensuring they meet the performance requirements for real-time embedded systems.
- Document and present experimental results, architectural choices, and technical findings to the team and stakeholders.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, or a related field.
- 3-8 years of professional experience in computer vision or machine learning application development.
- Proficiency in Python and a strong understanding of object-oriented programming.
- Strong hands-on experience with modern DL frameworks such as PyTorch or TensorFlow 2+.
- Solid understanding of deep learning fundamentals, including CNNs, object detection, and segmentation.
- A keen interest in learning and applying Transformers for vision is essential.
- Familiarity with essential software development tools like Git, Docker, and working in a Linux environment.
