About: Simplilearn Simplilearn is the world’s #1 online Bootcamp provider, enabling learners around the globe with rigorous and highly specialized training offered in partnership with world-renowned universities and leading corporations. We focus on emerging technologies and skills, such as data science, cloud computing, programming, and more — that are transforming the global economy. Our training is hands-on and immersive, including live virtual classes, integrated labs and projects, 24x7 support, and a collaborative learning environment. Over two million professionals and 2000 corporate training organizations across 150 countries have harnessed our award-winning programs to achieve their career and business goals. Simplilearn has collaborated with Fullstack Academy to leverage its widespread footprint in the US region and partnerships with Top US universities to grow internationally.

• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. • Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. • Partner with applied research to translate experimental improvements into production-ready systems. • Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. • Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. • Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. • Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. • Partner with applied research to translate experimental improvements into production-ready systems. • Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. • Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. • Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. • Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. • Partner with applied research to translate experimental improvements into production-ready systems. • Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. • Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. • Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.