Original listing text, shown exactly as published by the company.
About the role
We are looking for a Senior Machine Learning Engineer, 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, downstream language understanding, retrieval-augmented and agentic workflows, quality measurement, text to speech, 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, 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 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 We Value
- 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. This last item is an inference from the business context of all three roles, rather than a directly stated qualification.
