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Zendesk is hiring a Senior Machine Learning Manager to lead the vision, design, and implementation of scalable ML and generative AI systems, while mentoring a team and aligning ML initiatives with business goals. Key responsibilities include designing robust ML architectures, scaling ML productization, leading ML engineers and data scientists, engaging stakeholders, and promoting MLOps practices, CI/CD for models, monitoring, data contracts, and compliance. Ideal candidates have 5+ years in ML/DS roles with 2+ in leadership, a CS/Data Science degree (advanced preferred), deep expertise in ML system design and deployment on cloud platforms (AWS, Snowflake), plus Python proficiency and experience with data pipelines and the modern data stack (dbt, Astronomer). The role emphasizes business impact, cross-functional collaboration, clear communication, and thought leadership to influence Zendesk's technology roadmap and AI strategy, with opportunities to shape AI features at enterprise scale. The position offers a hybrid work model, global impact, an inclusive culture with a focus on diversity and inclusion, and accommodations for disabilities; Zendesk is an equal opportunity employer committed to flexibility, growth, and employee well-being.

Zendesk is seeking a Staff Machine Learning Engineer to level up its full-text search platform and support retrieval-augmented-generation across multiple channels, collaborating with Product Management, ML scientists, and engineers to deliver AI-powered capabilities at scale. The role involves delivering AI features using the latest LLM tech, mentoring junior engineers, maintaining the stability of deployed services, writing clean code, and contributing to technical design and best practices. Key challenges include expanding the RAG platform for new use cases, integrating hybrid search with vector embeddings and keyword retrieval, improving ranking, optimizing indexing speed and cost, and enabling cross-channel retrieval. Basic qualifications include proficiency in Python or Ruby, understanding software architecture patterns, a collaborative growth mindset, ability to work independently, experience building scalable systems, and capacity to formulate hypotheses and run experiments; preferred qualifications include designing ML-enabled search solutions (ideally with Elasticsearch), AWS deployment, and event-driven architectures with Kafka. The tech stack centers on Python and Ruby, PyTorch, AWS, data stores such as MySQL, Redis, S3, Elasticsearch, Kafka, Snowflake; services run on Kubernetes with Docker, while monitoring is done with Datadog and Sentry, and Zendesk offers an inclusive culture with growth opportunities and ownership of features at scale.