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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.
Job Responsibility:
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
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
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