Senior ML Engineer

Senior ML Engineer

Zendesk

Lisbon, Portugal

Job Summary

We are looking for a Staff Machine Learning Engineer to join our Machine Learning team. You will be delivering AI/ML powered features for our customers at Zendesk scale.

What you’ll be doing

• Work closely with ML Scientists to help bring ML-powered features into production and provide technical leadership across teams;
• Lead discussions about technical design and architecture;
• Build and guide the development of tools to support model experimentation and model management;
• Collaborate with DevOps, feature teams, platform teams, and product to drive initiatives and overcome technical obstacles;
• Lead initiatives around improving the scalability and robustness of our platforms;
• Design, prototype, and refine scalable infrastructure, influencing best practices and mentoring teams.

What you bring to the role

Required

• Fluent in at least one of Python or Ruby;
• At least 5 years of background in building, scaling, and operating large scale applications, with a demonstrated ability to drive technical direction and mentor others;
• Previous experience delivering ML products, or in ML operations (MLOps);
• Fluency with RESTful API, Kafka, Docker, Kubernetes, and cloud-like AWS or GCP;
• Ability to navigate ambiguity and provide direction with changing priorities;
• Deep respect and desire for team collaboration, while influencing and elevating team practices and technical standards;
• Desire and ability to build solutions utilizing AI/ML at scale.

Preferred

• Experience with either Airflow or Metaflow;
• Experience with recent tooling around LLM evaluation and monitoring;
• Familiar with one of the machine learning libraries such as Pytorch;
• Experience with either SQL or DBT.

Tech Stack

• Our code is written in Python and Ruby;
• Our servers live in AWS;
• Our local machine learning models rely on Pytorch;
• Our ML pipelines use Airflow and Metaflow;
• Our data is stored in S3, RDS MySQL and Snowflake;
• Our services and models are deployed to Kubernetes using Docker;
• Heavy usage of LLM technology from multiple providers.