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 Senior ML 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 own the delivery of ML-powered product features at Zendesk scale, taking capabilities from prototype through to production.
We ship to learn: our philosophy is to deliver early, deliver often, and iterate based on real-world customer feedback.
What you'll be doingOwn and deliver ML-powered product features end-to-end — from data pipelines and model integration through serving, monitoring, and iteration in production.
Work closely with Scientists to productionise research outputs into reliable, user-facing features.
Build and maintain ML infrastructure: model serving, inference pipelines, LLM integrations, and evaluation frameworks.
Contribute to technical design discussions and architecture decisions within your team, with growing influence across teams.
Collaborate with product software engineers to ensure ML capabilities are well-integrated into the broader product experience.
Improve the reliability, performance, and cost-efficiency of the ML systems you work on — proactively identifying and addressing issues.
Mentor more junior engineers through code review, pairing, and knowledge sharing.
Required
5+ years of experience in software engineering, with a meaningful focus on ML engineering, MLOps, or building ML-powered products.
Fluent in Python; working proficiency in Ruby is a plus.
Solid experience building and operating ML systems in production: model serving, inference pipelines, and monitoring.
Experience integrating LLMs into production systems — prompt engineering, evaluation, or multi-provider setups.
Comfortable with SQL and data infrastructure — you can work with data pipelines, transformations, and data quality.
Experience with containerised deployments (Docker, Kubernetes) and cloud infrastructure (AWS).
A track record of owning features end-to-end and delivering them to production with high quality.
Ability to work with uncertainty and the flexibility to pivot with changing priorities.
Strong collaboration skills — you work effectively with scientists, product engineers, and product managers.
Preferred
Experience with Snowflake and dbt for data transformations and analytics.
Hands-on experience with ML pipeline tooling (e.g., Metaflow) and experiment tracking (e.g., MLflow).
Experience with model serving frameworks (e.g., BentoML) on Kubernetes.
Familiarity with ML frameworks such as PyTorch or TensorFlow.
Experience with event-driven architectures (e.g., Kafka).
Experience with iterative, metrics-driven product development (A/B testing, feature flags, incremental rollouts).
Our code is written in Ruby and Python
Our servers live in AWS
Our ML pipelines use Metaflow
Our experiment tracking uses MLflow
Our models are served via BentoML on Kubernetes
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
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
- 5+ years of experience in software engineering with a meaningful focus on ML engineering, MLOps, or building ML-powered products.
- Fluent in Python.
- Working proficiency in Ruby.
- Solid experience building and operating ML systems in production: model serving, inference pipelines, and monitoring.
- Experience integrating LLMs into production systems (prompt engineering, evaluation, multi-provider setups).
- Comfortable with SQL and data infrastructure, including data pipelines, transformations, and data quality.
- Experience with containerised deployments (Docker, Kubernetes) and cloud infrastructure (AWS).
- Track record of owning features end-to-end and delivering them to production with high quality.
- Ability to work with uncertainty and pivot with changing priorities.
- Strong collaboration skills; work effectively with scientists, product engineers, and product managers.
- Experience with Snowflake and dbt for data transformations and analytics.
- Hands-on experience with ML pipeline tooling (e.g., Metaflow) and experiment tracking (e.g., MLflow).
- Experience with model serving frameworks (e.g., BentoML) on Kubernetes.
- Familiarity with ML frameworks such as PyTorch or TensorFlow.
- Experience with event-driven architectures (e.g., Kafka).
- Experience with iterative, metrics-driven product development (A/B testing, feature flags, incremental rollouts).
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.
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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.
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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.
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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.
Zendesk Insights
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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