Level

Zendesk

Senior Engineering Manager, Machine Learning

Zendesk is hiring a Senior Engineering Manager, Machine Learning in Melbourne, Australia. Level rates it ; you can apply on Zendesk.

Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include AI agents, AI evaluation and machine learning.
  • Level classifies it as a lead role.
  • Level lists 148 other open roles at Zendesk.
  • The role was posted in the last few days.

AI in this role

Lead a machine learning engineering team to build generative AI custom agents that reason, use tools, and automate workflows.

ai-agentsai-evaluationmachine-learninggenerative-aiteam-leadershipmlopsmodel-evaluation

Job Description

Senior Manager, AI - Custom Agents

About the group

Zendesk’s Custom Agents group enables customers to build specialized AI agents that understand business context, reason through complex tasks, and take actions across Zendesk and connected systems. These agents combine natural language instructions, enterprise knowledge, and tools to automate work such as investigating requests, interpreting documents, applying policies, and coordinating service workflows.

Our focus is making these capabilities reliable, configurable, and practical for real business operations.

About the role

We’re looking for a Senior Manager, Machine Learning to lead the development of the ML capabilities behind Custom Agents. You will set technical direction, build and develop a strong team, and turn advances in generative AI into dependable customer experiences.

This role combines people leadership, applied ML judgment, and accountability for production delivery. You will partner closely with Product, Engineering, Design, ML Science, and the Agent Platform team to improve how agents reason, use tools, follow policies, and complete tasks. You will guide decisions across model quality, reliability, latency, and cost, connecting technical investments to measurable customer value.

What you’ll do

  • Lead and grow the team. Hire, coach, and develop ML engineers and scientists. Build a culture of ownership, thoughtful experimentation, inclusion, and technical excellence, while developing the team’s next generation of leaders.
  • Set the ML strategy and roadmap. Translate customer needs and product goals into clear priorities, investment decisions, and achievable milestones. Balance foundational improvements with delivery of new capabilities.
  • Improve agent reasoning and execution. Guide work on planning, tool selection, context management, knowledge grounding, and structured outputs so agents consistently complete the intended task.
  • Establish rigorous evaluation. Develop representative benchmarks, human annotation processes, automated scorers, and regression tests. Measure task completion, tool-use correctness, policy adherence, and whether reported outcomes match actual execution.
  • Own quality throughout the lifecycle. Set standards for experimentation, validation, release readiness, and production monitoring. Use execution traces and customer feedback to diagnose failures and drive improvements.
  • Optimize performance and economics. Evaluate models and agent architectures against quality, latency, and cost. Make pragmatic decisions about model selection, routing, fallbacks, and inference efficiency.
  • Build trustworthy agent behavior. Partner with Security and Privacy teams on permission-aware actions, prompt injection defenses, data handling, and appropriate escalation or human approval.
  • Partner across the platform. Adopt and help shape shared capabilities for simulation, observability, experimentation, and durable execution. Work with adjacent teams to integrate Custom Agents into broader service workflows.
  • Deliver measurable customer impact. Connect ML improvements to successful automation, reduced handling time, customer adoption, and trust. Communicate progress, tradeoffs, and risks clearly to stakeholders.

What you bring

  • A demonstrated track record of leading ML or AI teams and delivering production systems with measurable customer impact.
  • Strong people management skills, including hiring, coaching, performance management, and developing technical leaders.
  • Deep understanding of modern ML and generative AI systems, with practical experience taking LLM applications from experimentation into production.
  • Technical judgment across model behavior, software architecture, evaluation, and operational reliability, with the ability to guide design reviews and assess experimental results.
  • Experience establishing meaningful quality metrics and using data to prioritize improvements and make release decisions.
  • The ability to lead through ambiguity, coordinate multiple workstreams, and influence partners across Product, Engineering, Science, and Security.
  • Clear communication and a pragmatic approach to balancing scientific rigor, delivery speed, and customer needs.

Additional experience we value

  • Building agents that use tools, retrieve enterprise knowledge, or coordinate workflows across systems.
  • Working with LLM evaluation, human feedback, multimodal document processing, or agent safety.
  • Leading distributed teams or delivering ML products in enterprise SaaS, customer service, or business automation.

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

Senior Engineering Manager, Machine Learning at Zendesk rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  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

AI agentsAI EvaluationMachine learningGenerative AITeam LeadershipMLOpsModel Evaluation

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. How do you decide that one model's output is better than another's for a given task?
  3. Tell me about a project where machine learning was part of your work. What did you do?
  4. Tell me about a project where generative ai was part of your work. What did you do?
  5. Tell me about a project where team leadership was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI agents, AI Evaluation, Machine learning, Generative AI, and Team Leadership. 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.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

Want an expert to read your CV for this job?

Free. Send your CV and the role you want next. We reply by email within 2 to 4 business days.

Get new data jobs (Builds AI ●●●●) by email

One email a week with the new data jobs (Builds AI ●●●●), each rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.

Free. One email a week. Unsubscribe in one click.

Similar roles

Data roles that build AI, at other companies.

What kind of AI work fits you?

Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.

Find my next step

More jobs at Zendesk

Related searches

Same AI level

Jobs by city