Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioral best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster. Born from the prestigious Stanford AI lab, Cresta's co-founder and chairman isSebastian Thrun, the genius behind Google X, Waymo, Udacity, and more. Our leadership also includes CEO,Ping Wu, the co-founder of Google Contact Center AI and Vertex AI platform,and co-founder, Tim Shi, an early member of Open AI.
Join us on this thrilling journey to revolutionize the workforce with AI. The future of work is here, and it's 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.
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 recruiting@cresta.ai