Machine Learning Engineer

You’ll design, develop, and deploy real-world machine learning systems that facilitate and accelerate advanced AI solutions at scale. As part of a core product team that thrives in an “internal start up” culture, you’ll drive decision-making with engineering creativity, and build state-of-the-art analytics libraries, products, services, and tooling that solve critical client problems.

You’ve operationalized machine learning while building scalable, robust, and secure products in a commercial environment. A keen problem solver, you engineer beautiful code using the Python scientific stack, use technology to work out complex analytical problems, and have:

• been exposed to different stages of machine learning system design and development
• utilized tools for machine learning pipeline management and monitoring (for example, MLflow, Pachyderm, Kubeflow, Seldon, Grafana)
• gained familiarity with automation and deployment (CircleCI/Jenkins/github actions, etc) and infrastructure as code (Terraform, CloudFormation, etc) technologies
• employed distributed processing frameworks such as Spark and Dask, and interacted with cloud platforms and container technologies
• gained practical knowledge of software engineering concepts and best practices, like testing frameworks, packaging, API design, DevOps, DataOps and MLOps
• developed excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks
• gained an advanced degree in computer science, engineering, or mathematics, or have equivalent experience