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Job Description Zendesk's people have one goal in mind: to make Customer Experience better.
Our products help more than 125,000 global brands (AirBnb, Uber, JetBrains, Slack, among others) make their billions of customers happy, every day.
Our team is responsible for helping Customer Experience teams to achieve their best, by intelligently solving repetitive work, so they can shift their focus to solving more sophisticated problems.
We use the latest trends in Machine Learning and AI algorithms to help us on that mission, and we're passionate about empowering our customers.
As a Staff Machine Learning Scientist you are a recognized leader and domain expert, responsible for advancing the state-of-the-art in ML/AI for customer service at a global scale.
You steer research vision, mentor scientists across teams, and drive adoption of foundational models powering Zendesk's most impactful features.
What you get to do every day Lead end-to-end design, development, and deployment of novel ML/LLM models and algorithms-defining research agendas that shape Zendesk's AI-powered roadmap.
Pioneer large, complex initiatives across product lines, such as building multilingual, real-time language/intent/sentiment analysis frameworks, conversational AI agents, and next-generation agent-assist solutions.
Define and drive experimentation standards, statistically robust offline/online evaluations, and model governance for compliance, fairness, and explainability.
Bridge cutting-edge research and production, collaborating with Engineering to build systems that scale globally and meet real-world performance constraints.
Mentor, guide, and develop Senior Scientists and Engineers, fostering a culture of scientific rigor, creativity, and technical excellence across the organization.
Represent Zendesk externally-publishing papers, speaking at conferences, and engaging with the wider ML/AI community.
Advise leadership on ML/AI technology strategy and assess emerging industry trends for integration into Zendesk solutions.
Mentor junior scientists and help grow the ML research culture.
Key challenges / use cases How do we enrich customer service conversations with accurate language detection, intent recognition, and real-time sentiment analysis, to enable proactive customer engagement and optimal routing?
How can we automate all customer service interactions as much as possible, from process automation to agent assistance and chatbots with a knowledge base?
How do we optimize routing at scale-matching tickets or chats to the most appropriate agent/team in real-time across multiple languages and regions?
How do we automate large-scale A/B testing and model evaluation (online and offline) to continually iterate and improve ML-driven triage and agent-assist tools?
What novel approaches or architectures (e.G