Quantitative Researcher and Developer - Portfolio Implementation

Responsibilities

• Utilize state-of-the-art model architectures, including transformers, for multi-modality tasks.
• Curate and manage datasets from real-world scenarios to train models effectively.
• Conduct training using large datasets and models on multi-GPU setups, demonstrating proficiency in data parallelism and model parallelism.
• Develop efficient and well-documented code while actively participating in code reviews.
• Deploy trained models on diverse platforms, such as robotic systems with ROS integration.
• Design and implement user-friendly demo interfaces.
• Optimize models for real-world applications, prioritizing accuracy, robustness, and speed.
• Innovate and implement novel fusion algorithms to leverage complementary information from different modalities, with applications spanning transportation and healthcare domains.

Required Qualifications

• Master’s degree or higher in Computer Science, Mathematics, or related fields; PhD preferred.
• Deep understanding of digital signal processing (e.g., Fourier transform), speech signal processing, image processing, and point-cloud processing.
• Profound knowledge of Artificial Intelligence principles and techniques.
• Proficiency in Python programming.
• Hands-on experience with at least one of TensorFlow, Keras, or PyTorch.