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 is Sebastian 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, the AI Agent team is on a mission to create state-of-the-art AI Agents that solve practical problems for our customers. We are focused on leveraging the latest technologies in Large Language Models (LLMs) and AI Agent systems, while ensuring that the solutions we develop are cost-effective, secure, and reliable. This role will involve hands-on work on cutting-edge projects, requiring innovative and passionate machine learning engineers who can bring research into practical, scalable applications.
As a Staff Machine Learning Engineer, your goal will be to take AI Agents from the realm of research and bring them into practical, real-world use cases. This includes developing and deploying proprietary LLMs, scaling AI solutions, and addressing key challenges such as evaluation and reliability. While weâre focused on real-world application rather than pure research, youâll be working with some of the most advanced technologies in the GenAI space.
This is a unique opportunity to shape the future of AI at Cresta by solving complex problems and bringing breakthrough AI advancements into production environments.
Responsibilities:
- Design, develop, and deploy Crestaâs AI Agent solutions and proprietary models.
- Focus on practical AI challenges such as improving reasoning, planning capabilities, and evaluation in real-world scenarios.
- Collaborate with cross-functional teams including front-end and back-end.
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