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Zendesk

Staff Software Engineer - AI Copilot (Backend)

Zendesk is hiring a Staff Software Engineer - AI Copilot (Backend) for a remote role open to applicants in Portugal. Level rates it ; you can apply on Zendesk.

Level's read on this job

  • On Level's scale, this software engineer role is Little AI ●○○○: aI is not part of the work.
  • The posting lists this role as remote.
  • Tools and skills the posting cites include Copilot, Vercel AI SDK and MLflow.
  • Level lists 143 other open roles at Zendesk.
  • The role was posted in the last few days.

AI in this role

CopilotVercel AI SDKMLflow
prompt-engineeringllm-integration

Job Description

Job Summary
At Zendesk, our focus is helping our customers build great relationships with their customers. Founded by three Danish entrepreneurs, Zendesk has experienced remarkable success and growth while maintaining a fun, positive, and down-to-earth culture.
We are looking for a Staff Engineer to join our AI Copilot organisation. AI Copilot is a multi-million ARR product that puts AI directly into the hands of customer service agents and administrators. You will be a technical leader driving the architecture and delivery of our conversational AI backend — the system that enables admins to configure and optimize Zendesk through natural language.
We ship to learn: our philosophy is to deliver early, deliver often, and iterate based on real-world customer feedback.

What you'll be doing

  • Drive the technical vision and architecture for the Admin Copilot Conversational backend — LLM orchestration, intent detection, context retrieval, and tool execution.
  • Own end-to-end delivery of complex, cross-cutting features — from technical design through implementation, release, and production operation.
  • Be a hands-on technical leader: set engineering standards, make key architecture decisions, and ensure the team builds reliable, scalable systems.
  • Collaborate closely with ML Scientists, Product Management, Design, and partner engineering teams to shape the product roadmap from a technical perspective.
  • Influence technical direction beyond your team — contribute to architecture decisions across the Copilot organisation and adjacent teams.
  • Design and improve evaluation frameworks, guardrails, and safety mechanisms for LLM-powered features in production.
  • Mentor and grow engineers on the team through technical guidance, code review, and pairing. Raise the bar for engineering quality.
  • Identify and address systemic technical risks, performance bottlenecks, and reliability gaps before they become customer-facing problems.

What you bring to the role
Required

  • 8+ years of experience in software engineering, with demonstrated technical leadership in backend systems.
  • Strong experience designing and building distributed systems at scale — APIs, service architectures, and data pipelines.
  • Experience with LLM integration in production systems — orchestration, prompt engineering, evaluation, guardrails, or multi-provider setups.
  • Fluent in at least one backend language (Ruby, Python, Go, or similar); working proficiency across multiple languages.
  • Deep understanding of cloud infrastructure (AWS), containerised deployments (Docker, Kubernetes), and CI/CD practices.
  • A track record of driving technical direction for a team or project — architecture decisions, technical roadmaps, and cross-team alignment.
  • Ability to operate effectively with ambiguity — you can break down complex problems, make pragmatic tradeoffs, and move fast without sacrificing quality.
  • Strong collaboration and communication skills — you influence through technical credibility and clear articulation of tradeoffs, not authority.


Preferred

  • Experience building conversational AI or chat-based systems (intent detection, context management, tool use).
  • Experience with ML infrastructure: model serving, inference pipelines, or evaluation frameworks.
  • Familiarity with ML pipeline tooling (e.g., Metaflow) and experiment tracking (e.g., MLflow).
  • Experience with event-driven architectures (e.g., Kafka).
  • Experience with SQL and data infrastructure (Snowflake, dbt).
  • Experience mentoring or technically leading a team of 3+ engineers.


Tech Stack

  • Our code is written in Ruby and Python
  • Our servers live in AWS
  • Our ML pipelines use Metaflow
  • Our data is stored in S3, RDS MySQL, and Snowflake (with dbt for transformations)
  • Our services and models are deployed to Kubernetes using Docker
  • Heavy usage of LLM technology from multiple providers via our LLM Proxy
  • Our conversational backend uses the Vercel AI SDK for LLM orchestration

The intelligent heart of customer experience

Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.

Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.

As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.

Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, & inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here.

Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to peopleandplaces@zendesk.com with your specific accommodation request.

How we rate this

Staff Software Engineer - AI Copilot (Backend) at Zendesk rates 6 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringLLM IntegrationCopilotVercel AI SDKMLflow

Also common in software engineer jobs at this level

Pay

Typical software engineer job
$168k

This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How have you integrated a large language model into a production application?
  3. What are the limits of Copilot that you've run into, and how did you work around them?
  4. What's a project where you used Vercel AI SDK hands-on?
  5. Walk me through how you've used MLflow in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Prompt engineering, LLM Integration, Copilot, Vercel AI SDK, and MLflow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.

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