Machine Learning Engineer

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, community-oriented, and down-to-earth culture.

Many years ago we recognised the power of Machine Learning to predict outcomes and guide people to potential answers when they have a question. We have built and launched a number of Machine Learning powered products such as Answer Bot, Macro Suggestion, Content Cues and Intelligent Triage. Our goal is to bring data driven smarts to our flagship Guide and Support products.

We are looking for a Machine Learning Engineer to help data scientists productionize Machine Learning models while helping feature teams in adding value to products. You will have a dramatic impact on how 145,000+ businesses around the world help their customers!

What you’ll be doing

• Build software to move Machine Learning from experiment to production

• Collaborate with ML Scientists to help bring ML-powered features into production

• Collaborate with DevOps, feature teams, platform teams and product

• Contribute to initiatives around improving the scalability and robustness of our platforms

• Design, prototype, and refine scalable infrastructure

• Attend and participate in Journal Clubs to learn about Machine Learning

• Build really cool products with a great team

What you bring to the roleRequired:

• Some familiality with Python

• Ability to work with uncertainty and the flexibility to pivot with changing priorities

• Deep respect and desire for team collaboration and members perspectives

Preferred:

• Previous ML Engineering Experience

• Familiarity with data engineering tools. E.g. Spark

• Familiarity with AI/ML workflows and associated tooling. E.g. SageMaker, ML Flow

Tech Stack:

• Our code is written in Python

• Our servers live in AWS

• Our team manages infrastructure using AWS CloudFormation 

• Our data is stored in S3, Redis, MySQL,  and Aurora

• Our services are deployed to Kubernetes using Docker

Summary of role requirements:

• Flexible hours available
• 2-3 years of relevant work experience required for this role
• Working rights required for this role
• Expected salary: $100,000 per year