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
