Senior Engineering Manager, Machine Learning

Posted 3 Days Ago
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2 Locations
In-Office or Remote
Senior level
Software
Zendesk is a service-first CRM company that builds software designed to improve customer relationships.
The Role
Leads the ML organization developing Zendesk Custom Agents, setting strategy, managing and mentoring engineers and scientists, and delivering production generative AI systems. Oversees agent reasoning, tool use, knowledge grounding, evaluation, reliability, safety, latency, and cost optimization. Partners across Product, Engineering, Design, Security, and Science to establish quality standards, monitor production performance, and connect ML improvements to measurable customer outcomes.
Summary Generated by Built In
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 [email protected] with your specific accommodation request.

Skills Required

  • 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 machine learning and generative AI systems
  • Practical experience taking large language model applications from experimentation into production
  • Technical judgment across model behavior, software architecture, evaluation, and operational reliability
  • Experience establishing quality metrics and using data to prioritize improvements and make release decisions
  • Ability to lead through ambiguity, coordinate multiple workstreams, and influence cross-functional partners
  • Clear communication and pragmatic judgment balancing scientific rigor, delivery speed, and customer needs
  • Experience building agents that use tools, retrieve enterprise knowledge, or coordinate workflows across systems
  • Experience with LLM evaluation, human feedback, multimodal document processing, or agent safety
  • Experience leading distributed teams or delivering ML products in enterprise SaaS, customer service, or business automation

Zendesk Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Zendesk and has not been reviewed or approved by Zendesk.

  • Fair & Transparent Compensation — The company states a commitment to publishing base pay ranges and advancing pay equity, helping employees gauge fairness. Public messaging on pay equity and transparency signals structured, consistent compensation practices.
  • Leave & Time Off Breadth — Time away programs include flexible PTO, dedicated well‑being days, emergency time off, and pregnancy loss leave. Parental leave is described as generous, and travel support exists for reproductive care where access is restricted.
  • Healthcare Strength — Benefits language highlights comprehensive medical, dental/vision, mental health access, and an employee assistance program. These offerings are positioned as part of holistic wellbeing support across regions.

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The Company
HQ: San Francisco, CA
6,277 Employees
Year Founded: 2007

What We Do

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. We advocate for digital first customer experiences— and we stick with it in our workplace. Over 5,000 employees worldwide are collaborating from kitchen tables, home offices, co-working spaces, and Zendesk workspaces to make one team.

Why Work With Us

We know one desk doesn’t fit all. At Zendesk, we prioritize remote work because we believe great work happens anywhere. Digital first is more than where we work though. We give our employees flexibility and choice in both where and how they work while also trusting them to be a team player.

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