Deep Learning Engineer

Responsibilities:

• Design and develop deep learning models and algorithms for various applications, such as computer vision, natural language processing, and speech recognition.
• Collect, preprocess, and analyze large-scale datasets to train deep neural networks.
• Implement and optimize deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models.
• Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to define project requirements and objectives.
• Stay up-to-date with the latest advancements in deep learning research and technologies, and apply them to enhance model performance.
• Conduct experiments and perform rigorous testing to evaluate and improve the accuracy and efficiency of deep learning models.
• Optimize and fine-tune deep learning models for deployment in production systems, considering factors such as computational resources and real-time constraints.
• Write clean, efficient, and maintainable code in languages such as Python, and utilize deep learning libraries and frameworks like TensorFlow, PyTorch, or Keras.
• Apply techniques like transfer learning, data augmentation, and regularization to improve model generalization and reduce overfitting.
• Collaborate with the data engineering team to ensure data pipelines and infrastructure support efficient training and deployment of deep learning models.
• Document and communicate research findings, methodologies, and best practices to stakeholders and team members.
• Mentor and provide guidance to junior team members, and contribute to the continuous learning and development of the deep learning team.

Requirements:

• Bachelor's or Master's degree in computer science, electrical engineering, or a related field. A Ph.D. is a plus.
• Solid understanding of machine learning principles and algorithms, particularly deep learning techniques.
• Proficiency in programming languages such as Python, with experience in using deep learning frameworks (e.g., TensorFlow, PyTorch, Keras).
• Experience in developing and training deep neural networks for various tasks, such as image classification, object detection, sequence modeling, or natural language understanding.
• Strong knowledge of linear algebra, calculus, probability, and statistics, and their application in deep learning.
• Familiarity with data preprocessing, feature engineering, and data visualization techniques.
• Hands-on experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and distributed computing frameworks (e.g., TensorFlow Distributed, Horovod) is preferred.
• Strong problem-solving skills and the ability to think critically and creatively in developing innovative deep learning solutions.
• Excellent communication and teamwork skills, with the ability to collaborate effectively with both technical and non-technical stakeholders.
• A strong portfolio or track record of deep learning projects, research publications, or contributions to the deep learning community is a plus.