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.

The AI/ML Platform team is at the forefront of this mission. We build the foundation that powers every AI-driven experience at Zendesk, enabling product teams to build, evaluate, and deploy state-of-the-art Large Language Model (LLM) applications reliably and at scale.

We're looking for a Senior ML Engineer to lead the next wave of GenAI infrastructure at Zendesk. This includes our internal research platform, LLM Proxy, A/B Testing & Evaluation benchmarking, agentic workflow orchestration tools. You’ll empower Zendesk’s ML/AI teams by building secure, cost-optimized, and developer-friendly ML platforms that scale across use cases and products.

You’ll work closely with Staff Engineers, Tech Leads, Product Managers, and other ML teams to deliver robust, production-grade systems that accelerate the impact of AI across Zendesk.

What you get to do every day

• Help build benchmarking frameworks for LLMs, including A/B, Offline Evals testing capabilities to assess quality, latency, and cost trade-offs.
• Contribute to the design and implementation of Zendesk’s LLM Proxy to enable safe, observable, and cost-optimized access to multiple foundation models.
• Partner with applied ML, product, and platform teams to ensure GenAI infrastructure meets the needs of diverse product use cases.
• Implement best practices for monitoring, observability, rate-limiting, and cost attribution for LLM services.
• Establish strong engineering practices around observability, reliability, security, and cost monitoring.
• Work on orchestration tooling to enable multi-step, tool-using AI agents that integrate with Zendesk’s products
  
  

What you bring to the role

• 5+ years in developing and deploying ML systems in production, with hands-on experience in scaling infrastructure and ensuring service reliability.
• Familiarity with core ML infrastructure components such as model registries, feature stores, orchestration tools, and inference serving systems.
• Understanding of LLM systems, GenAI applications, or ML/AI platform components such as vector databases, serving layers, and orchestration tools.
• Experience with GCP, AWS, or Azure; Kubernetes; Docker; and distributed systems.
• Proficiency in at least one server-side language (Python, Java, Scala, Golang, or Ruby) and solid grounding in testing and CI/CD workflows.
• Understanding of architecture principles and patterns for building scalable, resilient backend services.
• Experience taking projects from design to production deployment, with a focus on maintainability and performance.
  
  

Preferred Qualifications

• Agentic and Automation: Experience with AI technologies in automating processes and developing agentic solutions and frameworks
• Experience building tools that improve developer productivity and platform adoption across multiple teams.
  
  

What Our Tech Stack Looks Like

• Our code is written in Python.
• Our servers live in AWS.
• LLM Vendors: OpenAI, Anthropic, Google, Llama
• Infra: Kubernetes, Docker, Kafka, AWS