We are seeking a talented and experienced Machine Learning Engineer specializing in image-based networks to join our dynamic team.
As a Machine Learning Engineer, you will play a pivotal role in developing and deploying state-of-the-art models and algorithms for tasks such as image generation, recommender engines, prediction models, and more. Your work will directly contribute to advancing our cutting-edge machine learning capabilities.

QUALIFICATIONS:

• Proven industry experience (2+ years) in developing and deploying deep learning machine learning models.
• Solid understanding of deep learning concepts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and/or graph neural networks (GNNs).
• Strong programming skills in Python, including proficiency in one or more deep learning frameworks (TensorFlow, PyTorch, Keras). PyTorch preferred.
• Experience with image processing techniques, computer vision libraries (OpenCV), and related tools.
• Familiarity with AWS infrastructure and toolchain (SageMaker, CloudFormation, CloudWatch, etc.)
• Ability to preprocess and manipulate large datasets using tools such as NumPy, Pandas, and scikit-learn.
• Knowledge of software engineering principles, including version control (Git) and agile development methodologies.
• Excellent problem-solving skills, with the ability to work on complex machine learning challenges independently.
• Strong written and verbal communication skills, with the ability to effectively collaborate with team members and present findings to stakeholders.

• Analyze and preprocess large-scale datasets for training and evaluation purposes.
• Experiment with different architectures, loss functions, and data augmentation techniques to improve model performance.
• Collaborate with cross-functional teams to define project requirements and deliver innovative solutions.
• Stay up-to-date with the latest advancements in machine learning and computer vision, and apply them to solve complex problems.
• Troubleshoot and debug issues related to model training, performance, and scalability.
• Integrate the training software into our continuous integration cluster to support metrics persistence across experiments, weekly/nightly neural network builds, and other unit / throughput tests.
• Collaborate with software engineers to integrate machine learning models into production systems.
• Document research findings, experiments, and algorithms in technical reports and presentations.