As a MLOps engineer, you will be responsible for designing, building, and maintaining production-grade machine learning pipelines, as well as sharing the best practice of MLOps to other team members. You will help deploy state of the art AI technology, build tools, and act as the bridge between application developers and machine learning research. You will have the opportunity to work on applications with millions of active users, as well as bring brand new applications to market.

• Fluency in Python.
• Experience working with data and machine learning platforms, such as KubeFlow, MLflow, Airflow, etc.
• Experience working with containerization technologies such as Docker and Kubernetes.
• Experience with cloud platforms such as GCP, AWS, or Azure.
• Experience working with machine learning frameworks such as PyTorch or Tensorflow.
• Experience with API design and implementation.
• Strong understanding of software engineering and machine learning best practices.

Responsibilities:

• Design, build and maintain data and machine learning pipelines to make them easy-to-use and scalable.
• Monitor and optimize the data and machine learning pipelines.
• Automate and simplify the process of deploying machine learning models.
• Apply and share software engineering best practice to machine learning.
• Take models and turn them into a machine learning production system.

After you submit your application, the next steps are one or two video interview calls (with short written questions), followed by an onsite interview. Our interview process includes interesting exercises with our team members (e.g. pair programming, design brainstorming). Through this process, we hope you can get to know what it's like to work with us, and get to know our team and culture!

Please note that only applications through the link will be considered!