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

We are seeking a Machine Learning Engineer to design and deliver innovative features and models that advance our ML systems. In this role, you will scale model evaluation workflows, build robust data pipelines, and optimize performance across the stack. Your responsibilities will include: * Pipeline Scaling & Optimization: Design, build, and maintain scalable ETL/ELT data pipelines using tools like Spark, & Airflow to handle large-scale datasets. Optimize existing pipelines for efficiency, latency, and cost. * Data Augmentation & Synthesis: Research and implement advanced data augmentation techniques (e.g., GANs, semantic augmentation, synthetic data generation) to address data scarcity and imbalanced datasets. * Data Quality & Monitoring: Implement data observability and automated data validation checks to identify data drift, schema violations, and outliers in real-time. * Failure Analysis & Debugging: Perform root-cause analysis on production model failures, diagnosing issues between data inputs and model outputs using advanced statistical methods. * Model Evaluation: Collaborate with other machine learning engineers to productize models, implementing robust evaluation frameworks, including experimentation and performance monitoring. Proficiency in working with unstructured data, specifically video & audio signals, for object detection, pattern recognition, feature extraction and segmentation. Proficiency with Python and deep learning frameworks like PyTorch. Expertise in designing metrics, and conducting metric change & performance analysis for model evaluation. Strong problem solving skills in analyzing complex, ambiguous problems and clearly presenting sophisticated technical concepts to both expert and non-expert audiences. Master’s degree or equivalent experience in a technical or quantitative field. Experience with shipping ML features and products Strong verbal and written communications skills with demonstrated experience in authoring & presenting analytical insights via papers & presentations. Self-motivated and curious with creative and critical thinking capabilities and drive to figure out and improve how things work. High tolerance for ambiguity. You find a way through. You anticipate. You connect and synthesize. Experience with large scale training ML models including deep learning based models. Experience with GPU-based distributed training & evaluation. Background in Computer Vision (image augmentation), Audio and Natural Language Processing.

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