You will develop and improve machine learning systems that power voice experiences end to end, including automatic speech recognition, turn detection, downstream language understanding, retrieval-augmented and agentic workflows, quality measurement, text to speech, and production optimization.

Responsibilities:

• Design, train, evaluate, and deploy machine learning systems that power real-time voice experiences, including ASR, speech understanding, turn detection, text to speech, speech to speech, classification, entity extraction, summarization, and structured insight generation.
• Improve the quality of voice AI systems through error analysis, data curation, metric design, benchmarking, and iterative model improvement, with a strong focus on real-world performance.
• Build evaluation frameworks for complex voice and agentic systems, measuring metrics such as accuracy, robustness, latency, faithfulness, naturalness, professionalism, task completion, and cost.
• Diagnose and mitigate failure modes across the voice stack, including transcription errors, hallucinations, retrieval failures, tool misuse, prompt brittleness, context drift, and multi-step reasoning breakdowns.
• Design and optimize low-latency ML workflows for live conversations, balancing model quality with system responsiveness, scalability, and reliability.
• Partner with platform and backend engineers to productionize real-time inference, streaming pipelines, quality monitoring, and continuous model iteration.
• Collaborate cross-functionally with product, design, frontend, and backend teams to integrate voice intelligence seamlessly into Cresta’s platform.
• Establish best practices for offline evaluation, online experimentation, model validation, observability, and ongoing quality monitoring in production.
• Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta’s voice AI systems.

Qualifications:

• Bachelor’s degree in Computer Science, Mathematics, Machine Learning, AI, or a related field; Master’s or Ph.D. preferred.
• 5+ years of experience building, evaluating, and deploying machine learning systems in production.
• Strong background in one or more of the following: speech recognition, speech processing, NLP, generative AI, or conversational AI.
• Deep experience with model evaluation, benchmarking, error analysis, and quality improvement for production ML systems.
• Strong expertise with modern ML frameworks and tooling such as PyTorch, TensorFlow, and Hugging Face.
• Solid understanding of transformer-based models, embeddings, retrieval systems, and large-scale training or inference workflows.
• Experience designing and deploying real-time ML systems with strong requirements around latency, scalability, and reliability.
• Experience building data pipelines and tooling for experimentation, measurement, and large-scale quality analysis.
• Ability to work across research and engineering boundaries and translate promising ideas into production-grade systems.
• Strong communication and technical leadership skills, with the ability to influence cross-functional decisions and raise the engineering bar.

Nice to have:

• Hands-on experience with ASR quality metrics such as WER and task-level evaluation methodologies.
• Experience with RAG systems, agentic workflows, multi-step reasoning systems, or LLM-as-a-judge evaluation methods.
• Familiarity with streaming inference, real-time voice pipelines, or media systems.
• Experience working closely with infrastructure or platform teams on production ML deployment, observability, and reliability.
• Experience in contact center AI, conversational intelligence, or enterprise voice products.

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