Deep Learning Engineer Job

Develop and leverage neural network architectures (U-net and SDM) and deep supervision to learn image translation with an accuracy close to physics-based simulation. Use deep learning frameworks such as TensorFlow, Tensorboard, Keras to build, train and infer neural networks. Build an end-to-end system and flow of neural network model development, encompassing model building, rigorous training, and effective debugging for optimal performance in production. Execute training and inference of deep learning models efficiently on GPU clusters in a distributed manner, optimizing performance and scalability in production environments. Write efficient code in TCL, Python, and C++ for building model, data, and inference pipelines, adhering to the company's software design principles and coding guidelines. Visualize and analyze complex proprietary datasets to extract insights, identify patterns, and debug model behavior, ensuring data-driven decision-making. Utilize NVIDIA libraries such as CUDA, cuDNN, and TensorRT to optimize the underlying deep learning model training and inference speed on GPUs.

Minimum Requirements:
Education: Master’s degree or foreign degree equivalent in Computer Information Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or related field.
Experience: Two (2) years of experience in job offered, AI Engineer, Machine Learning Researcher, Application Developer, or in a related occupation.

Annual salary: $226,138 - $227,000/yr.

How to apply: Submit your resume via email to [email protected]. Must reference Ref# DLE-AK

Education or experience in:
Academic training or work experience in the following:
1. Machine Learning and Neural Networks
2. CNN based computer vision algorithms and image processing
3. Python and C++ languages
4. Building, training and deployment of deep learning models in a distributed manner using TensorFlow

Submit your resume via email to [email protected] . Must reference Ref# DLE-AK