AIML - Machine Learning Engineer - Computer Vision & Audio, MIND

What you'll do

• Design and maintain scalable ETL/ELT data pipelines using tools like Spark and Airflow for large-scale datasets.
• Implement advanced data augmentation techniques to address data scarcity and imbalanced datasets, enhancing model performance.
• Develop automated data validation checks to monitor real-time data quality issues such as drift and schema violations.
• Conduct root-cause analysis on production model failures, diagnosing issues between data inputs and outputs using statistical methods.
• Collaborate with teams to implement robust evaluation frameworks for experimentation and continuous monitoring of model performance.

What we're looking for

• Proficiency in working with unstructured data, specifically video and audio signals for object detection and pattern recognition.
• Expertise in Python and deep learning frameworks like PyTorch for model development.
• Strong ability to design metrics and analyze performance changes for model evaluation.
• Experience in scaling ETL/ELT data pipelines using tools like Spark and Airflow.
• Capability to perform root-cause analysis on production model failures using statistical methods.
• Background in computer vision, audio processing, and natural language understanding.
• Self-motivated with creative thinking skills to improve existing systems and processes.