Job Summary
Senior AI / Machine Learning Engineer will build, train, and optimize machine learning models for content understanding, ranking, and scoring. You will transform research concepts into robust, production-ready ML pipelines and maintain high-quality code to drive impactful AI solutions.
Responsibilities
Implement deep learning models for video, image, audio, or text using modern frameworks to enhance content understanding and ranking capabilitiesDevelop and maintain training and evaluation pipelines, including data loaders, augmentations, and metrics, to ensure reliable model performanceDesign and execute experiments, tune hyperparameters, and optimize models for latency and accuracy to meet production requirementsCollaborate with backend engineers to deploy models via scalable APIs and services, enabling seamless integration into production systemsMonitor model performance in production environments and design retraining and improvement loops to sustain and enhance model effectivenessContribute to the development of ML tooling, experiment tracking systems, and comprehensive documentation to support team productivity and knowledge sharingRequired competencies and certifications
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field4–7+ years of experience as an ML Engineer, Applied Scientist, or equivalent roleProficiency in PyTorch or TensorFlow and common machine learning libraries to develop and optimize modelsExperience in at least one domain: computer vision, video processing, speech/audio processing, or natural language processing (NLP)Strong understanding of model optimization techniques such as quantization, pruning, distillation, and batching to improve model efficiencyFamiliarity with GPU training, distributed training, and cloud-based ML workflows to support scalable model developmentStrong coding skills in Python and solid grasp of software engineering best practices to produce maintainable and efficient codePreferred competencies and qualifications
Master’s degree in Machine Learning, Artificial Intelligence, Data Science, or related field preferred but not mandatory with sufficient experienceExperience with MLOps tooling such as MLflow or Weights & Biases (W&B) to streamline model lifecycle managementBackground in Kaggle competitions or similar machine learning contests demonstrating practical problem-solving skillsPrior experience working in startup environments, contributing to fast-paced and innovative projects
