Senior Machine Learning Engineer

Senior Machine Learning Engineer

Regular price

Job Summary

At Zendesk’s we 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 thoughtfully 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.

We are looking for a Senior Machine Learning Engineer to deliver our scientists’ ML models to our customers at scale. The ideal candidate should have strong software engineer skills, experience in ML pipelines, and a deep interest in building machine learning products.

What you’ll be doing

  • Build and maintain a scalable end to end production ML system used by thousands of customers
  • Build and improve existing data and model building pipelines at scale
  • Work closely with ML scientists to help bring ML-powered features into production
  • Contribute to and lead discussions about technical design
  • Support model experimentation and manage 1000+ NLP models in production
  • Collaborate with feature teams, platform teams, and product management teams
  • Contribute to initiatives around improving the scalability, reliability, observability, and robustness of our platforms
  • Work alongside teammates in EMEA and APAC regions

What you bring to the role

Basic Qualifications

  • 6+ years of full-time proven experience in software engineering, with a minimum of 2+ years of machine learning exposure
  • Solid understanding of algorithms, data structures, and object-oriented design
  • Ability to navigate the stack from infrastructure to application and the mechanics behind deployment and scaling
  • Experience working with product and engineering to scope, design, build, deploy and serve ML features
  • Proficiency in one or more of the following languages: Python and Java/Scala
  • Familiarity with messaging systems, Docker, Kubernetes, Spark, EMR, and cloud platforms (AWS or GCP)
  • Familiarity with one of the main machine learning libraries - - preferably Tensorflow with PyTorch and Scikit-Learn also valued
  • Experience with the observability, performance, and security of the system
  • Ability to work with uncertainty and flexibility to adjust with changing priorities
  • A passion for engineering solutions that use ML/AI
  • A collaborative and can-do mindset with a desire to learn and grow

Preferred Qualifications

  • Experience with big data storage and processing frameworks such as EMR, Hadoop, Spark knowledge
  • Knowledge of natural language processing
  • Experience in building large-scale data and model build pipelines

Tech Stack

  • Our code is written in Python, Java, and Scala
  • Our servers live in AWS
  • Our machine learning models rely on Tensorflow and Scikit-Learn
  • Our models are deployed on AWS Sagemaker
  • Our ML pipelines use AWS StepFunctions, Batch, and EMR
  • Our team manages infrastructure using AWS CloudFormation and Chef
  • Our data are stored in S3, RDS, Redis, ElasticSearch, and Aurora
  • Our services are deployed to Kubernetes using Docker with Spinnaker pipelines

About Zendesk - Champions Of Customer Service

Zendesk started the customer experience revolution in 2007 by enabling any business around the world to take their customer service online. Today, Zendesk is the champion of great service everywhere for everyone, and powers billions of conversations, connecting more than 100,000 logos with hundreds of millions of customers over telephony, chat, email, messaging, social channels, communities, review sites and help centers. Zendesk products are built with love to be loved. The company was conceived in Copenhagen, Denmark, built and grown in California, taken public in New York City, and today employs more than 4,000 people across the world. Learn more at

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