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 capabilities
• Develop and maintain training and evaluation pipelines, including data loaders, augmentations, and metrics, to ensure reliable model performance
• Design and execute experiments, tune hyperparameters, and optimize models for latency and accuracy to meet production requirements
• Collaborate with backend engineers to deploy models via scalable APIs and services, enabling seamless integration into production systems
• Monitor model performance in production environments and design retraining and improvement loops to sustain and enhance model effectiveness
• Contribute to the development of ML tooling, experiment tracking systems, and comprehensive documentation to support team productivity and knowledge sharing

Required competencies and certifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
• 4–7+ years of experience as an ML Engineer, Applied Scientist, or equivalent role
• Proficiency in PyTorch or TensorFlow and common machine learning libraries to develop and optimize models
• Experience 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 efficiency
• Familiarity with GPU training, distributed training, and cloud-based ML workflows to support scalable model development
• Strong coding skills in Python and solid grasp of software engineering best practices to produce maintainable and efficient code

Preferred competencies and qualifications

• Master’s degree in Machine Learning, Artificial Intelligence, Data Science, or related field preferred but not mandatory with sufficient experience
• Experience with MLOps tooling such as MLflow or Weights & Biases (W&B) to streamline model lifecycle management
• Background in Kaggle competitions or similar machine learning contests demonstrating practical problem-solving skills
• Prior experience working in startup environments, contributing to fast-paced and innovative projects