Lead Engineer - AI Agent Voice Experience

Cresta; United States; Remote; USD 150.000 - 210.000; Vollzeit

Zusammenfassung

Cresta is looking for a Lead Engineer, AI Agent Voice Experience to help build the next generation of AI-powered voice systems for the contact center. You will work at the intersection of speech, language, and real-time production systems, improving how AI listens, understands, reasons, empathizes, and responds in live customer conversations.

You will develop and improve machine learning systems that power voice experiences end to end, including automatic speech recognition, turn detection,

Aufgaben

• Design, train, evaluate, and deploy ML systems powering real-time voice experiences, including speech to text turn detection, text to speech, speech to speech, reasoning and structured insight generation.
• Improve the quality of voice AI systems via 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, tool misuse, prompt brittleness, context drift, and multi-step reasoning breakdowns.

Jobbeschreibung

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

We are looking for a Lead Engineer, AI Agent Voice Experience to help build the next generation of AI-powered voice systems for the contact center. In this role, you will work at the intersection of speech, language, and real-time production systems, improving how AI listens, understands, reasons, empathizes, and responds in live customer conversations.

You will develop and improve machine learning systems that power voice experiences end to end, including automatic speech recognition, turn detection, agentic workflows, voice evaluation, text to speech, speech-to-speech models, and production optimization. You will partner closely with applied researchers, product managers, designers, forward deployed engineers, and platform engineers to ensure model and system improvements translate into measurable customer and business impact.

This role is ideal for someone who is excited by both model quality and production reality: designing rigorous evaluation frameworks, analyzing failure modes, improving latency and robustness, elevating caller interaction experience, and shipping systems that perform reliably at scale in real-time voice environments.

Responsibilities

• Design, train, evaluate, and deploy machine learning systems that power real-time voice experiences, including speech to text turn detection, text to speech, speech to speech, reasoning 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, tool misuse, prompt brittleness, context drift, and multi-step reasoning breakdowns.
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