Zendesk’s people have one goal in mind: to make Customer Experience better. Our products help more than 125,000 global brands (AirBnb, Uber, JetBrains, Slack, among others) make their billions of customers happy, every day.

Our team is responsible for helping Customer Experience teams to achieve their best, by intelligently solving repetitive work, so they can shift their focus to solving more sophisticated problems. We use the latest trends in Machine Learning and AI algorithms to help us on that mission, and we're passionate about empowering our customers.

As a Senior ML Engineer, you’ll be responsible for developing products in collaboration with our Data Scientists and other Machine Learning engineers, and delivering high-quality ML and AI products to our customers, at a scale that most companies only dream of.

What you get to do every day:

• Write clean and maintainable code to support the team’s delivery commitments.
• Work closely with Data Science and Product teams to tackle hard problems Zendesk customers face, using AI and ML technology.
• Collaborate closely with fellow Engineers to find innovative solutions to traditional technical challenges.
• Actively contribute to discussions about technical design and standard methodology.
• Support the building of MLOps processes in the team.
• Investigate and resolve production issues.
• Champion initiatives to improve the scalability and robustness of our platforms.

What you bring to the role:

• Proficiency in at least one of our core languages: Python, Scala, or Java. Experience in Spark is a bonus too!
• A discernible passion for data engineering: especially around large-scale data processing.
• Exposure to AWS infrastructure is an advantage.
• A self-managed and dedicated approach with the ability to work independently.
• Strong problem-solving capabilities as well as the flexibility (of working style) to deal with changing and conflicting priorities.
• Experience building scalable and stable software applications.
• Willingness to mentor junior team members, as well as to learn and improve.

What our tech stack looks like:

• Our code is written in Scala, Ruby, Python, Java, and Go.
• Our servers live in AWS.
• Our machine learning models rely on PyTorch and Tensorflow.
• Our ML pipelines use AWS Batch and EMR.
• Our data is stored in S3, RDS MySQL, Redis, ElasticSearch, and Aurora.
• Our services are deployed to Kubernetes using Docker, and use Kafka for stream-processing.