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Senior Machine Learning Engineer Automatic Speech Recognition (ASR)Cresta • United States
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Senior Machine Learning Engineer Automatic Speech Recognition (ASR)

Cresta
  • US
    United States
  • US
    United States
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About

Senior Machine Learning Engineer Automatic Speech Recognition (ASR)
Germany (Remote) Cresta unlocks the true potential of the customer experience, turning every conversation into a competitive advantage. Cresta's unified AI platform combines conversational AI agents, real-time human agent augmentation, and comprehensive conversation intelligence to drive revenue and efficiency gains across every channel. The world's leading companies, including United Airlines, Cox Communications, and Marriott, use Cresta to power world-class customer experiences every day. Born from the Stanford AI Lab, Cresta has raised more than $270 million from the world's leading investors, including a16z, Greylock, and Sequoia. Cresta's leadership includes some of the leading minds in AI today. Our CEO, Ping Wu, founded and led Google's Contact Center AI and Vertex AI platforms before joining Cresta to build the future of AI-driven customer experiences. Over the next few years, AI is going to redefine how people all over the world interact with businesses every day. Come build that future at Cresta. About the Role
At Cresta, we are dedicated to building state-of-the-art Machine Learning systems that power real-time, intelligent customer interactions. Our team develops models and platforms that process large-scale, multimodal data—especially speech and text—to extract meaning, improve quality, and deliver actionable insights at scale. By combining applied research with strong engineering discipline, we enable organizations to continuously improve AI-driven experiences in production environments. A key focus of this role is advancing model evaluation, measurement, and quality improvements, with particular emphasis on Automatic Speech Recognition (ASR) and downstream NLP systems. You will design rigorous evaluation frameworks, define quality metrics, and drive systematic improvements to model accuracy, robustness, and reliability. You will work closely with applied researchers, product teams, and platform engineers to ensure that model performance improvements translate into measurable business impact. As a Senior Machine Learning Engineer, you will be at the forefront of applying modern ML and speech/NLP techniques to production systems. Your work will focus on improving ASR quality, building scalable evaluation and benchmarking infrastructure, and enabling continuous model iteration through data-driven insights. Responsibilities 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. Qualifications We Value 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. Deep experience with model evaluation, benchmarking, and error analysis for ML systems. 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.
  • United States

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  • English
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