Staff ML Engineer - AI Search & RAG Platform

Zendesk; Lisboa; Híbrido; EUR 91 000 - 137 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

RAG projects and AI initiatives

Ownership of features at scale

Learn and grow opportunities

Specialise in security, performance, &

Resumo da oferta

Zendesk is seeking a Staff Machine Learning Engineer to elevate our search platform, powering AI-driven experiences for millions of customers. You will work with PMs, ML scientists, and engineers to define scope and deliver high-quality features that improve retrieval-augmented-generation across channels.

You will mentor teammates, optimize our indexing and ranking pipelines, and help design scalable architectures on AWS, Elasticsearch and Kakfa-based streams.

Qualificações

• Proficiency in Python or Ruby with testing frameworks.
• Strong understanding of server-side and web app architecture.
• Ability to work independently and continue learning.

Responsabilidades

• Deliver AI-powered capabilities at Zendesk scale using the latest LLM tech.
• Define feature scope and implementation strategy with PMs and ML teams.
• Mentor junior engineers and pair with seniors to foster learning.
• Maintain reliability and stability of deployed services.
• Write clean, maintainable code to meet delivery commitments.
• Contribute to design discussions and best practices.

Conhecimentos

Python

Ruby

Testing frameworks

Software architecture

Collaboration

Independence

Scalable apps

Experimentation

ML basics

Ferramentas

Elasticsearch

AWS

Kafka

Descrição da oferta de emprego

Zendesk is seeking a Staff Machine Learning Engineer to elevate our search platform, powering AI-driven experiences for millions of customers. You will work with PMs, ML scientists, and engineers to define scope and deliver high-quality features that improve retrieval-augmented-generation across channels.

You will mentor teammates, optimize our indexing and ranking pipelines, and help design scalable architectures on AWS, Elasticsearch and Kakfa-based streams.