• deliver AI/ML models from initial planning through to deployment • create AI-based prototypes to evaluate model and design options • train and tune models to optimize algorithmic outcomes • integrate models into production systems • conduct research into emerging AI models and technologies • support operational excellence and customer-centricity

• Implement AI-powered services and application features that integrate enterprise data and systems with LLMs and ML models • Contribute to solution design by proposing approaches, identifying dependencies, and documenting tradeoffs and implementation plans • Deliver features using strong engineering practices: test automation, code review, CI/CD, and operational readiness • Build and tune RAG pipelines and implement prompt patterns • Contribute to evaluation suites and regression testing to ensure quality and safety • Implement guardrails and security controls under guidance from senior engineers • Instrument services with metrics, traces, logs, and dashboards • Support performance and cost optimization efforts

• Lead end-to-end design, development, and deployment of novel ML/LLM models and algorithms • Pioneer large, complex initiatives across product lines, such as building multilingual, real-time language/intent/sentiment analysis frameworks • 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 • Mentor, guide, and develop Senior Scientists and Engineers • Represent Zendesk externally—publishing papers, speaking at conferences, and engaging with the wider ML/AI community.

• Build and scale automated evaluation systems for AI models. • Develop and train automated “judge” models to assess AI outputs. • Design validation frameworks to ensure accuracy and reliability across languages. • Create synthetic data pipelines to improve evaluation coverage. • Build scalable analysis and reporting systems for AI performance. • Work closely with language experts and cross-functional teams.