AI Research Engineer

Role Overview

We are seeking an Applied AI Researcher / Machine Learning Research Engineer to join a dynamic AI product team focused on developing tangible AI solutions. This position emphasizes deployment and production rather than experimental research, requiring direct contributions to AI models and systems intended for real-world applications.

Key Responsibilities

• Design, train, refine, assess, and enhance deep learning models tailored for practical AI uses.
• Engage with neural network frameworks such as Convolutional Neural Networks (CNNs) and Transformer architectures.
• Perform experiments to validate the models' accuracy, reliability, and ability to generalize against new, unseen datasets.
• Choose and implement appropriate loss functions, architectural designs, and training protocols relevant to the task.
• Analyze model outputs, detect weaknesses, and systematically improve precision, robustness, and computational efficiency.
• Convert research insights into production-ready AI solutions ready for deployment.
• Collaborate closely with cross-functional teams including product management, engineering, and business units to ensure AI functionalities are scalable, practical, and aligned with product goals.
• Focus all model development efforts on achieving measurable impact in deployed environments.

Candidate Requirements

• A Bachelor's degree in Computer Science, Engineering, Mathematics, Artificial Intelligence, Machine Learning, Data Science, or related domains is required; advanced degrees (Master’s, PhD) are preferred.
• Proven practical experience working with neural network architectures, with specialization in CNNs and/or Transformer-based models.
• Demonstrated competency in training, fine-tuning, assessing, and enhancing deep learning AI models.
• Deep understanding of core deep learning principles, including model design, loss function selection, optimization techniques, and training behavior.
• Capability to rigorously evaluate model generalizability to data beyond training examples.
• Hands-on proficiency with machine learning frameworks such as PyTorch and/or TensorFlow.
• Strong programming ability, particularly with Python.
• Effective communication skills to lucidly articulate technical decisions covering architecture, loss functions, and trade-offs in model performance.