• Lead the design, prototyping, and deployment of scalable machine learning infrastructure that can power Zillow AI Mode. • Serve as a critical technical liaison between the Agentic Data Platform and applied AI teams. • Collaborate closely with scientists, engineers, and product partners to understand their dependencies and deliver the data foundations they need to innovate rapidly. • Drive alignment on timelines, architectures, and prioritization across different organizations, influencing technical direction without requiring direct authority. • Deploy and optimize capabilities in production environments, ensuring high availability, low latency, and efficient resource utilization capable of handling massive scale. • Mentor other engineers, guiding them in using the right technologies and establishing best practices. • Translate complex ML infrastructure designs and ambiguous customer problems into clear, actionable insights for diverse audiences, including leadership and non-technical stakeholders.

• Successful candidates will teach Computer Science courses in the online format • Specific course needs are as follows: AI and Machine Learning, Data Mining, Applied Artificial Intelligence, Machine Learning, Computer Vision and Sensors, Decision Networks and Graphs.

• Design, develop, and optimize machine learning and deep learning models using PyTorch • Build and deploy computer vision solutions for real-world use cases • Develop end-to-end ML pipelines, including data ingestion, preprocessing, training, validation, and deployment • Implement and maintain MLOps workflows for model versioning, monitoring, CI/CD, and retraining • Deploy and scale ML models on AWS cloud infrastructure • Work with large-scale datasets using Databricks and distributed computing frameworks • Collaborate with data scientists, product managers, and software engineers to translate business requirements into AI solutions • Ensure high code quality by following software engineering best practices • Monitor model performance in production and continuously improve accuracy, efficiency, and reliability

• 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.