Cresta - Senior Machine Learning Engineer - Automatic Speech Recognition (ASR)
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Requirements
• Master’s or Ph.D. in Computer Science, Machine Learning, AI, or a related field. • 5+ years of hands-on experience building, evaluating, and deploying ML models in production. • Strong background in speech recognition (ASR), speech processing, or closely related domains. • speech recognition (ASR) • Deep experience with model evaluation, benchmarking, and error analysis for ML systems. • model evaluation, benchmarking, and error analysis • Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face). • Solid understanding of modern ML techniques, including transformer-based models and large-scale training. • Experience building data pipelines and tooling for large-scale experimentation and quality analysis. • Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements. • Compensation for this position includes a base salary, equity, and a variety of benefits. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable. • This posting will be used to fill a newly-created role. • We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Cresta recruiting email communications will always come from the @cresta.ai domain. Any outreach claiming to be from Cresta via other sources should be ignored. If you are uncertain whether you have been contacted by an official Cresta employee, reach out to [email protected]
Responsibilities
• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • evaluation frameworks • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • ASR quality improvement efforts • 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.
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